diff --git a/README.md b/README.md index 137589b..b20b831 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# SDNist v2.3: Deidentified Data Report Tool +# SDNist v2.4: Deidentified Data Report Tool ## [SDNist is the offical software package for engaging in the NIST Collaborative Research Cycle](https://pages.nist.gov/privacy_collaborative_research_cycle) @@ -37,7 +37,7 @@ Setting Up the SDNIST Report Tool ### Brief Setup Instructions -SDNist requires Python version 3.7 or greater. If you have installed a previous version of the SDNist library, we recommend installing v2.3 in a virtual environment. v2.3 can be installed via [Release 2.3](https://github.com/usnistgov/SDNist/releases/tag/v2.3.0) or via the Pypi server: `pip install sdnist` or, if you already have a version installed, `pip install --upgrade sdnist`. +SDNist requires Python version 3.7 or greater. If you have installed a previous version of the SDNist library, we recommend installing v2.4 in a virtual environment. v2.4 can be installed via [Release 2.4](https://github.com/usnistgov/SDNist/releases/tag/v2.4) or via the Pypi server: `pip install sdnist` or, if you already have a version installed, `pip install --upgrade sdnist`. The NIST Diverse Community Exceprt data will download on the fly. diff --git a/sdnist/metrics/kmarginal.py b/sdnist/metrics/kmarginal.py index f28a467..0cf8f28 100644 --- a/sdnist/metrics/kmarginal.py +++ b/sdnist/metrics/kmarginal.py @@ -58,10 +58,13 @@ def __init__(self, self.features = self.td.columns.tolist() marg_cols = list(set(self.features).difference(['PUMA', 'INDP'])) marg_cols = sorted(marg_cols) - self.marginals = [(f1, f2) - for i, f1 in enumerate(marg_cols) - for j, f2 in enumerate(marg_cols) - if i < j] + if len(marg_cols) == 1: + self.marginals = [(marg_cols[0], marg_cols[0])] + else: + self.marginals = [(f1, f2) + for i, f1 in enumerate(marg_cols) + for j, f2 in enumerate(marg_cols) + if i < j] def marginal_pairs(self): for _ in self.marginals: diff --git a/sdnist/metrics/pca.py b/sdnist/metrics/pca.py index 2dcf53d..f880ced 100644 --- a/sdnist/metrics/pca.py +++ b/sdnist/metrics/pca.py @@ -44,7 +44,7 @@ def __init__(self, target: pd.DataFrame, synthetic: pd.DataFrame,): self.comp_df = None def compute_pca(self): - cc = 5 + cc = 5 if self.tar.shape[1] > 5 else self.tar.shape[1] t_pca = PCA(n_components=cc) tdf_v = self.tar.values @@ -185,10 +185,12 @@ def plot_all_components_pairs(title: str, plt.close(fig) fig, ax = plt.subplots(cc, cc, figsize=(6, 6)) - for i, pc_i in enumerate(d.columns): for j, pc_j in enumerate(d.columns): - ax_t = ax[i, j] + if cc == 1: + ax_t = ax + else: + ax_t = ax[i, j] if pc_i == pc_j: ax_t.text(0.5, 0.5, pc_i, ha='center', va='center', color='black', fontsize=30) diff --git a/sdnist/metrics/unique_exact_matches.py b/sdnist/metrics/unique_exact_matches.py index ef22520..7e63dbc 100644 --- a/sdnist/metrics/unique_exact_matches.py +++ b/sdnist/metrics/unique_exact_matches.py @@ -26,10 +26,13 @@ def unique_exact_matches(target_data: pd.DataFrame, deidentified_data: pd.DataFr # number of unique target records that exactly match in deidentified data t_rec_matched = merged.shape[0] - # percent of unique target records that exactly match in deidentified data - perc_t_rec_matched = t_rec_matched/t_unique_records * 100 + if t_unique_records > 0: + # percent of unique target records that exactly match in deidentified data + perc_t_rec_matched = t_rec_matched/t_unique_records * 100 - perc_t_rec_matched = round(perc_t_rec_matched, 2) + perc_t_rec_matched = round(perc_t_rec_matched, 2) + else: + perc_t_rec_matched = 0 return t_rec_matched, perc_t_rec_matched, t_unique_records, perc_t_unique_records diff --git a/sdnist/report/dataset/__init__.py b/sdnist/report/dataset/__init__.py index df33c1d..24eb4d3 100644 --- a/sdnist/report/dataset/__init__.py +++ b/sdnist/report/dataset/__init__.py @@ -66,7 +66,7 @@ def feature_space_size(target_df: pd.DataFrame, data_dict: Dict): 'DEAR']: size = size * len(data_dict[col]['values']) elif col in ['PUMA', 'DENSITY']: - size = size * len(target_df['PUMA'].unique()) + size = size * len(target_df[col].unique()) elif col in ['NOC', 'NPF', 'INDP']: size = size * len(target_df[col].unique()) diff --git a/sdnist/report/dataset/binning.py b/sdnist/report/dataset/binning.py index 801dde7..5bee3c1 100644 --- a/sdnist/report/dataset/binning.py +++ b/sdnist/report/dataset/binning.py @@ -32,6 +32,7 @@ def percentile_rank_synthetic(synthetic: pd.DataFrame, if f not in to.columns.tolist(): continue nna_mask = s[~s[f].isin(['N'])].index # not na mask + st = pd.DataFrame(pd.to_numeric(s.loc[nna_mask, f]).astype(int), columns=[f]) final_st = st.copy() max_b = 0 @@ -50,7 +51,7 @@ def percentile_rank_synthetic(synthetic: pd.DataFrame, else: min_b = max_b final_st.loc[(st[f] > min_b), f] = b - s.loc[nna_mask, f] = final_st + s.loc[nna_mask, f] = final_st[f] return s diff --git a/sdnist/report/dataset/validate.py b/sdnist/report/dataset/validate.py index 8048a16..3929ec0 100644 --- a/sdnist/report/dataset/validate.py +++ b/sdnist/report/dataset/validate.py @@ -26,9 +26,9 @@ def console_out(text: str): "nan_records": len(nan_df), "nan_features": nan_features } - sd = sd.dropna() - console_out(f'Found {len(nan_df)} records with NaN values. ' - f'Removed records with NaN values.') + # sd = sd.dropna() + # console_out(f'Found {len(nan_df)} records with NaN values. ' + # f'Removed records with NaN values.') for f in features: # check feature has out of bound value @@ -43,7 +43,8 @@ def console_out(text: str): fd.loc[mask, f] = pd.to_numeric(fd.loc[mask, f], errors="coerce") nans = fd[fd.isna().any(axis=1)] if len(nans): - vob_vals = list(set(synth_data.loc[nans.index, f].values.tolist())) + vob_vals = list(set([str(v) + for v in synth_data.loc[nans.index, f].values.tolist()])) vob_features.append((f, vob_vals)) console_out(f'Value out of bound for feature {f}, ' f'out of bound values: {vob_vals}. ' @@ -62,7 +63,8 @@ def console_out(text: str): f_unique = sd['PUMA'].unique().tolist() v_intersect = set(f_unique).intersection(set(f_vals)) if len(v_intersect) < len(f_unique): - vob_vals = list(set(f_unique).difference(v_intersect)) + vob_vals = list(set([str(v) for v in + list(set(f_unique).difference(v_intersect))])) vob_features.append((f, vob_vals)) console_out(f'Value out of bound for feature {f}, ' f'out of bound values: {vob_vals}. Dropping feature from evaluation.') @@ -73,7 +75,8 @@ def console_out(text: str): nans = fd[fd.isna().any(axis=1)] vob_vals = [] if len(nans): - vob_vals.extend(list(set(synth_data.loc[nans.index, f].values.tolist()))) + vob_vals.extend(list(set([str(v) + for v in synth_data.loc[nans.index, f].values.tolist()]))) fd = fd.dropna() mask = fd[fd[f] != 'N'].index if has_N else fd.index @@ -87,7 +90,8 @@ def console_out(text: str): real_vals = [int(v) for v in real_vals] v_intersect = set(f_unique).intersection(set(real_vals)) if len(v_intersect) < len(f_unique): - vob_vals.extend(list(set(f_unique).difference(v_intersect))) + vob_vals.extend(set([str(v) + for v in list(set(f_unique).difference(v_intersect))])) if len(vob_vals): vob_features.append((f, vob_vals)) @@ -102,11 +106,10 @@ def console_out(text: str): if len(vob_features): last_vob_f, vob_vals = vob_features[-1] - if last_vob_f == f: sd = sd.loc[:, sd.columns != f] if has_nan: - vob_features = (last_vob_f, ['nan'] + vob_vals) + vob_features[-1] = (last_vob_f, ['nan'] + list(set(vob_vals))) validation_log['values_out_of_bound'] = dict(vob_features) return sd, validation_log \ No newline at end of file diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_synopsys/subsample_error_comparison.csv b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_synopsys/subsample_error_comparison.csv deleted file mode 100644 index 9a0d4a0..0000000 --- a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_synopsys/subsample_error_comparison.csv +++ /dev/null @@ -1,11 +0,0 @@ -,Sub-Sample Size,Sub-Sample K-Marginal Score,Deidentified Data K-marginal score,Absolute Diff. From Deidentified Data K-marginal Score -0,10%,840,121,719 -1,20%,893,121,772 -2,30%,915,121,794 -3,40%,932,121,811 -4,50%,946,121,825 -5,60%,955,121,834 -6,70%,963,121,842 -7,80%,972,121,851 -8,90%,981,121,860 -9,100%,1000,121,879 diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/linear_regression/black_women/density_plot.svg b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/linear_regression/black_women/density_plot.svg deleted file mode 100644 index dc469dc..0000000 --- a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/linear_regression/black_women/density_plot.svg +++ /dev/null @@ -1,3524 +0,0 @@ - - - - - - - - 2023-05-19T18:02:30.394837 - image/svg+xml - - - Matplotlib v3.7.1, https://matplotlib.org/ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv index 17db10d..1148568 100644 --- a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv +++ b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv @@ -1,6 +1,6 @@ ,PUMA,40% Target Subsample Baseline,Deidentified Data Score -0,01-01301,811,0 -1,30-00600,885,0 -2,28-01100,856,0 +0,01-01301,813,0 +1,30-00600,886,0 +2,28-01100,851,0 3,51-51255,879,0 -4,08-00803,856,0 +4,08-00803,861,0 diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv similarity index 83% rename from sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv rename to sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv index ab94bee..4a37012 100644 --- a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv +++ b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv @@ -1,21 +1,21 @@ ,PUMA,40% Target Subsample Baseline,Deidentified Data Score -0,"['01-01301', 'Birmingham City (West)', 'https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/']",811,0 -1,"['30-00600', 'East Montana (Outside Billings City)', 'https://censusreporter.org/profiles/79500US3000600-east-montana-outside-billings-city-puma-mt/']",885,0 -2,"['28-01100', 'Central Region--Jackson City (East & Central)', 'https://censusreporter.org/profiles/79500US2801100-central-region-jackson-city-east-central-puma-ms/']",856,0 +0,"['01-01301', 'Birmingham City (West)', 'https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/']",813,0 +1,"['30-00600', 'East Montana (Outside Billings City)', 'https://censusreporter.org/profiles/79500US3000600-east-montana-outside-billings-city-puma-mt/']",886,0 +2,"['28-01100', 'Central Region--Jackson City (East & Central)', 'https://censusreporter.org/profiles/79500US2801100-central-region-jackson-city-east-central-puma-ms/']",851,0 3,"['51-51255', 'Alexandria City', 'https://censusreporter.org/profiles/79500US5151255-alexandria-city-puma-va/']",879,0 -4,"['08-00803', 'Boulder County (Central)--Boulder City', 'https://censusreporter.org/profiles/79500US0800803-boulder-county-central-boulder-city-puma-co/']",856,0 -5,"['06-08507', 'Santa Clara County (Southwest)--Cupertino, Saratoga Cities & Los Gatos Town', 'https://censusreporter.org/profiles/79500US0608507-santa-clara-county-southwest-cupertino-saratoga-cities-los-gatos-town-puma-ca/']",879,2 -6,"['32-00405', 'Las Vegas City (Southeast)', 'https://censusreporter.org/profiles/79500US3200405-las-vegas-city-southeast-puma-nv/']",857,5 -7,"['06-07502', 'San Francisco County (North & East)--North Beach & Chinatown', 'https://censusreporter.org/profiles/79500US0607502-san-francisco-county-north-east-north-beach-chinatown-puma-ca/']",854,6 -8,"['19-01700', 'Des Moines City', 'https://censusreporter.org/profiles/79500US1901700-des-moines-city-puma-ia/']",861,7 -9,"['29-01901', 'St. Louis City (North)', 'https://censusreporter.org/profiles/79500US2901901-st-louis-city-north-puma-mo/']",841,7 -10,"['17-03529', 'Chicago City (South)--South Shore, Hyde Park, Woodlawn, Grand Boulevard & Douglas', 'https://censusreporter.org/profiles/79500US1703529-chicago-city-south-south-shore-hyde-park-woodlawn-grand-boulevard-douglas-puma-il/']",866,8 -11,"['36-04010', 'NYC-Brooklyn Community District 17--East Flatbush, Farragut & Rugby', 'https://censusreporter.org/profiles/79500US3604010-nyc-brooklyn-community-district-17-east-flatbush-farragut-rugby-puma-ny/']",861,9 -12,"['40-00200', 'Cherokee, Sequoyah & Adair Counties', 'https://censusreporter.org/profiles/79500US4000200-cherokee-sequoyah-adair-counties-puma-ok/']",854,9 -13,"['24-01004', 'Montgomery County (South)--Bethesda, Potomac & North Bethesda', 'https://censusreporter.org/profiles/79500US2401004-montgomery-county-south-bethesda-potomac-north-bethesda-puma-md/']",902,9 -14,"['13-04600', 'Atlanta Regional Commission--Fulton County (Central)--Atlanta City (Central)', 'https://censusreporter.org/profiles/79500US1304600-atlanta-regional-commission-fulton-county-central-atlanta-city-central-puma-ga/']",857,10 -15,"['26-02702', 'Washtenaw County (East Central)--Ann Arbor City Area', 'https://censusreporter.org/profiles/79500US2602702-washtenaw-county-east-central-ann-arbor-city-area-puma-mi/']",844,14 -16,"['36-03710', 'NYC-Bronx Community District 1 & 2--Hunts Point, Longwood & Melrose', 'https://censusreporter.org/profiles/79500US3603710-nyc-bronx-community-district-1-2-hunts-point-longwood-melrose-puma-ny/']",834,15 -17,"['38-00100', 'West North Dakota--Minot City', 'https://censusreporter.org/profiles/79500US3800100-west-north-dakota-minot-city-puma-nd/']",885,25 +4,"['08-00803', 'Boulder County (Central)--Boulder City', 'https://censusreporter.org/profiles/79500US0800803-boulder-county-central-boulder-city-puma-co/']",861,0 +5,"['06-08507', 'Santa Clara County (Southwest)--Cupertino, Saratoga Cities & Los Gatos Town', 'https://censusreporter.org/profiles/79500US0608507-santa-clara-county-southwest-cupertino-saratoga-cities-los-gatos-town-puma-ca/']",887,2 +6,"['32-00405', 'Las Vegas City (Southeast)', 'https://censusreporter.org/profiles/79500US3200405-las-vegas-city-southeast-puma-nv/']",853,5 +7,"['06-07502', 'San Francisco County (North & East)--North Beach & Chinatown', 'https://censusreporter.org/profiles/79500US0607502-san-francisco-county-north-east-north-beach-chinatown-puma-ca/']",850,6 +8,"['19-01700', 'Des Moines City', 'https://censusreporter.org/profiles/79500US1901700-des-moines-city-puma-ia/']",862,7 +9,"['29-01901', 'St. Louis City (North)', 'https://censusreporter.org/profiles/79500US2901901-st-louis-city-north-puma-mo/']",840,7 +10,"['17-03529', 'Chicago City (South)--South Shore, Hyde Park, Woodlawn, Grand Boulevard & Douglas', 'https://censusreporter.org/profiles/79500US1703529-chicago-city-south-south-shore-hyde-park-woodlawn-grand-boulevard-douglas-puma-il/']",873,8 +11,"['36-04010', 'NYC-Brooklyn Community District 17--East Flatbush, Farragut & Rugby', 'https://censusreporter.org/profiles/79500US3604010-nyc-brooklyn-community-district-17-east-flatbush-farragut-rugby-puma-ny/']",849,9 +12,"['40-00200', 'Cherokee, Sequoyah & Adair Counties', 'https://censusreporter.org/profiles/79500US4000200-cherokee-sequoyah-adair-counties-puma-ok/']",852,9 +13,"['24-01004', 'Montgomery County (South)--Bethesda, Potomac & North Bethesda', 'https://censusreporter.org/profiles/79500US2401004-montgomery-county-south-bethesda-potomac-north-bethesda-puma-md/']",901,9 +14,"['13-04600', 'Atlanta Regional Commission--Fulton County (Central)--Atlanta City (Central)', 'https://censusreporter.org/profiles/79500US1304600-atlanta-regional-commission-fulton-county-central-atlanta-city-central-puma-ga/']",853,10 +15,"['26-02702', 'Washtenaw County (East Central)--Ann Arbor City Area', 'https://censusreporter.org/profiles/79500US2602702-washtenaw-county-east-central-ann-arbor-city-area-puma-mi/']",852,14 +16,"['36-03710', 'NYC-Bronx Community District 1 & 2--Hunts 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sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/linear_regression/white_women/target_deidentified_counts_difference.csv rename to sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/linear_regression/white_women/target_deidentified_counts_difference.csv diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/pca/components_eigenvector.csv b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/pca/components_eigenvector.csv new file mode 100644 index 0000000..c90b30a --- /dev/null +++ b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/pca/components_eigenvector.csv @@ -0,0 +1,6 @@ +,AGEP,DEAR,DENSITY,DEYE,DPHY,DREM,DVET,EDU,HISP,HOUSING_TYPE,INDP,INDP_CAT,MSP,NOC,NPF,OWN_RENT,PINCP,PINCP_DECILE,POVPIP,PUMA,PWGTP,RAC1P,SEX,WGTP 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sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/report.html rename to sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/report.html index aa32c22..3d6d83e 100644 --- a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/report.html +++ b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/report.html @@ -160,11 +160,11 @@

Data Evaluation Report


-

Report created on: May 19, 2023 18:02:44

+

Report created on: June 19, 2023 20:40:45

Created with - SDNIST v2.2.1 + SDNIST v2.3.0

@@ -275,7 +275,52 @@

- Library + Variant Label + + + + default, lambda=10 + + + + + + + + + + + Submission Number + + + + 1 + + + + + + + + + + + Algorithm Type + + + + neural net + + + + + + + + + + + Library Name @@ -290,7 +335,7 @@

- Feature Set + Feature Set Name @@ -305,11 +350,11 @@

- Variant Label + Privacy Category - default, lambda=10 + non_dp @@ -320,11 +365,11 @@

- Parameters + Deid Data Id - + 87d9498e671770dae31c84db1705c893846ea3c5 @@ -335,11 +380,26 @@

- Submission Number + Features List - 1 + PUMA, AGEP, SEX, MSP, HISP, RAC1P, NOC, NPF, HOUSING_TYPE, OWN_RENT, DENSITY, INDP, INDP_CAT, EDU, PINCP, PINCP_DECILE, POVPIP, DVET, DREM, DPHY, DEYE, DEAR, WGTP, PWGTP + + + + + + + + + + + Privacy Label Detail + + + + GDPR @@ -350,11 +410,11 @@

- Privacy + Research Papers - Synthetic Data (Non-differentially Private) + https://doi.org/10.1109/jbhi.2020.2980262 @@ -1257,7 +1317,7 @@

- K-Marginal score of the deidentified data closely resembles K-Marginal score of a 10% sub-sample of the target data. + K-Marginal score of the deidentified data closely resembles K-Marginal score of a 1% sub-sample of the target data.
@@ -1322,11 +1382,11 @@

- 10% + 1% - 840 + 565 @@ -1334,7 +1394,7 @@

- 719 + 444 @@ -1348,11 +1408,11 @@

- 20% + 5% - 893 + 774 @@ -1360,7 +1420,7 @@

- 772 + 653 @@ -1371,11 +1431,11 @@

- 30% + 10% - 915 + 842 @@ -1383,7 +1443,7 @@

- 794 + 721 @@ -1394,11 +1454,11 @@

- 40% + 20% - 932 + 891 @@ -1406,7 +1466,7 @@

- 811 + 770 @@ -1417,11 +1477,11 @@

- 50% + 30% - 946 + 918 @@ -1429,7 +1489,7 @@

- 825 + 797 @@ -1440,11 +1500,11 @@

- 60% + 40% - 955 + 932 @@ -1452,7 +1512,7 @@

- 834 + 811 @@ -1463,11 +1523,11 @@

- 70% + 50% - 963 + 946 @@ -1475,7 +1535,7 @@

- 842 + 825 @@ -1486,11 +1546,11 @@

- 80% + 60% - 972 + 955 @@ -1498,7 +1558,7 @@

- 851 + 834 @@ -1509,11 +1569,11 @@

- 90% + 70% - 981 + 964 @@ -1521,7 +1581,7 @@

- 860 + 843 @@ -1532,11 +1592,11 @@

- 100% + 80% - 1000 + 973 @@ -1544,7 +1604,30 @@

- 879 + 852 + + + + + + + + + + + 90% + + + + 981 + + + + 121 + + + + 860 @@ -1648,7 +1731,7 @@

- 811 + 813 @@ -1670,7 +1753,7 @@

- 885 + 886 @@ -1692,7 +1775,7 @@

- 856 + 851 @@ -1736,7 +1819,7 @@

- 856 + 861 @@ -1758,7 +1841,7 @@

- 879 + 887 @@ -1780,7 +1863,7 @@

- 857 + 853 @@ -1802,7 +1885,7 @@

- 854 + 850 @@ -1824,7 +1907,7 @@

- 861 + 862 @@ -1846,7 +1929,7 @@

- 841 + 840 @@ -1868,7 +1951,7 @@

- 866 + 873 @@ -1890,7 +1973,7 @@

- 861 + 849 @@ -1912,7 +1995,7 @@

- 854 + 852 @@ -1934,7 +2017,7 @@

- 902 + 901 @@ -1956,7 +2039,7 @@

- 857 + 853 @@ -1978,7 +2061,7 @@

- 844 + 852 @@ -2000,7 +2083,7 @@

- 834 + 833 @@ -2022,7 +2105,7 @@

- 885 + 889 @@ -2066,7 +2149,7 @@

- 841 + 828 @@ -5286,7 +5369,7 @@

- This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you’re using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes– the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive. + This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you're using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.
@@ -5569,7 +5652,7 @@

- The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP = “N”, individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. + The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP='N', individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape.
@@ -5679,6 +5762,34 @@

+
+ +
+ + + + +
+ In real world tabular data, it's common for record features to have some deterministic, publicly known dependencies on each other: knowing someone's AGEP= 3 necessarily tells you something about their marital status, income and educational attainment. Different deidentification methods may be better or worse at automatically preserving these relationships. When they fail (ex: producing toddlers with PhDs) we say those records contain "inconsistencies". Our consistency check metric below is not exhaustive, we don't catch everything the way a production-grade system should, but this will give you a sense of how consistency is preserved for age, work and household features. Note that different deidentification approaches may do better or worse with different types of inconsistencies. +
+ + + +
+ + + + + + + + + + + + + +

@@ -15002,7 +15113,7 @@

- K-Marginal Score Breakdown: + Worst Performing PUMAs Breakdown:

@@ -15026,7 +15137,7 @@

- In the metrics above we’ve considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score. + In the metrics above we've considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score.
@@ -15125,7 +15236,7 @@

- 811 + 813 @@ -15147,7 +15258,7 @@

- 885 + 886 @@ -15169,7 +15280,7 @@

- 856 + 851 @@ -15213,7 +15324,7 @@

- 856 + 861 @@ -15665,6 +15776,7 @@

Privacy Evaluation

+

@@ -15692,7 +15804,91 @@

- This is a count of unique records in the target data that were exactly reproduced in the deidentified data. Because these records were unique outliers in the target data, and they still appear unchanged in the deidentified data, they are potentially vulnerable to reidentification. + Unique Exact Match (UEM) is a simple privacy metric that counts the percentage of singleton records in the target that are also present in the deidentified data; these uniquely identifiable individuals leaked through the deidentification process. +
+ + + +

+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ Below we also include an estimate of the feature space size. The feature space is the set of all possible record values given the selected target data and feature subset. For instance, if we had two features, Hat [cap, bonnet] and Color [green, blue, purple], our feature space would consist of 2 x 3 = 6 possible combinations (e.g. 'green cap', 'blue bonnet'). Note that feature spaces are based on the feature set, not on what records actually exist in the data. Purple bonnet is a possible combination in this feature space, but it's likely no one in the hypothetical data owns a purple bonnet (and the count of that record value would be 0). +
+ + + +
+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ As we add features to the feature set, we increase the size of the feature space, but we don't change the actual number of records in the data-- it's the same people, but now they're spread out more thinly across a wider set of possible record values. Large feature spaces will disperse populations very sparsely (most possible record values will have count 0 or 1) and as a result the data will contain very many uniquely identifiable records. Intuitively, once you know enough pieces of information about someone, everyone becomes very distinct from everyone else. This can pose a challenge for privacy. +
+ + + +
+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ The Target Data Properties below provides an estimate of the feature space size (100 is used for continuous feature), along with the portion of the records in the ground truth target data that are unique (ie, they are the only person with that record value, they have a count of 1). The Deidentified Data Properties reports the percentage of those uniquely identifiable individuals that are still present in the deidentified data. Because they are unique, real records, they are potentially vulnerable to reidentification.
@@ -15736,7 +15932,7 @@

- 3,407,717,673,360,000 + 5.178e+26 @@ -15825,6 +16021,12 @@

+
+
+
+
+ +

@@ -15915,7 +16117,7 @@

- OWN_RENT, RAC1P, SEX, PUMA, HISP, INDP_CAT, EDU, MSP + HISP, OWN_RENT, PUMA, INDP_CAT, EDU, RAC1P, MSP, SEX
@@ -18789,6 +18991,34 @@

+
+ +
+ + + + +
+ There are a total of 271 possible codes for INDP, 269 of these codes appear in the Diverse Community Data Excerpts (233 in MA, 264 in Texas and National) +
+ + + +
+ + + + + + + + + + + + + +

diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/ui.json b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/ui.json similarity index 95% rename from sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/ui.json rename to sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/ui.json index 8adac04..39c658f 100644 --- a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/ui.json +++ b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/ui.json @@ -1,6 +1,6 @@ { - "Created on": "May 19, 2023 18:02:44", - "version": "2.2.1", + "Created on": "June 19, 2023 20:40:45", + "version": "2.3.0", "data_description": { "target": [ { @@ -55,28 +55,44 @@ "Label Value": "adsgan" }, { - "Label Name": "Library", + "Label Name": "Variant Label", + "Label Value": "default, lambda=10" + }, + { + "Label Name": "Submission Number", + "Label Value": 1 + }, + { + "Label Name": "Algorithm Type", + "Label Value": "neural net" + }, + { + "Label Name": "Library Name", "Label Value": "synthcity" }, { - "Label Name": "Feature Set", + "Label Name": "Feature Set Name", "Label Value": "all-features" }, { - "Label Name": "Variant Label", - "Label Value": "default, lambda=10" + "Label Name": "Privacy Category", + "Label Value": "non_dp" }, { - "Label Name": "Parameters", - "Label Value": "" + "Label Name": "Deid Data Id", + "Label Value": "87d9498e671770dae31c84db1705c893846ea3c5" }, { - "Label Name": "Submission Number", - "Label Value": 1 + "Label Name": "Features List", + "Label Value": "PUMA, AGEP, SEX, MSP, HISP, RAC1P, NOC, NPF, HOUSING_TYPE, OWN_RENT, DENSITY, INDP, INDP_CAT, EDU, PINCP, PINCP_DECILE, POVPIP, DVET, DREM, DPHY, DEYE, DEAR, WGTP, PWGTP" }, { - "Label Name": "Privacy", - "Label Value": "Synthetic Data (Non-differentially Private)" + "Label Name": "Privacy Label Detail", + "Label Value": "GDPR" + }, + { + "Label Name": "Research Papers", + "Label Value": "https://doi.org/10.1109/jbhi.2020.2980262" } ], "validations": [] @@ -254,7 +270,7 @@ }, { "name": null, - "data": "K-Marginal score of the deidentified data closely resembles K-Marginal score of a 10% sub-sample of the target data.", + "data": "K-Marginal score of the deidentified data closely resembles K-Marginal score of a 1% sub-sample of the target data.", "type": "string", "dotted_break": false }, @@ -262,23 +278,35 @@ "name": null, "data": [ { - "Sub-Sample Size": "10%", - "Sub-Sample K-Marginal Score": 840, + "Sub-Sample Size": "1%", + "Sub-Sample K-Marginal Score": 565, "Deidentified Data K-marginal score": 121, - "Absolute Diff. From Deidentified Data K-marginal Score": "719", + "Absolute Diff. From Deidentified Data K-marginal Score": "444", "min_idx": true }, + { + "Sub-Sample Size": "5%", + "Sub-Sample K-Marginal Score": 774, + "Deidentified Data K-marginal score": 121, + "Absolute Diff. From Deidentified Data K-marginal Score": "653" + }, + { + "Sub-Sample Size": "10%", + "Sub-Sample K-Marginal Score": 842, + "Deidentified Data K-marginal score": 121, + "Absolute Diff. From Deidentified Data K-marginal Score": "721" + }, { "Sub-Sample Size": "20%", - "Sub-Sample K-Marginal Score": 893, + "Sub-Sample K-Marginal Score": 891, "Deidentified Data K-marginal score": 121, - "Absolute Diff. From Deidentified Data K-marginal Score": "772" + "Absolute Diff. From Deidentified Data K-marginal Score": "770" }, { "Sub-Sample Size": "30%", - "Sub-Sample K-Marginal Score": 915, + "Sub-Sample K-Marginal Score": 918, "Deidentified Data K-marginal score": 121, - "Absolute Diff. From Deidentified Data K-marginal Score": "794" + "Absolute Diff. From Deidentified Data K-marginal Score": "797" }, { "Sub-Sample Size": "40%", @@ -300,27 +328,21 @@ }, { "Sub-Sample Size": "70%", - "Sub-Sample K-Marginal Score": 963, + "Sub-Sample K-Marginal Score": 964, "Deidentified Data K-marginal score": 121, - "Absolute Diff. From Deidentified Data K-marginal Score": "842" + "Absolute Diff. From Deidentified Data K-marginal Score": "843" }, { "Sub-Sample Size": "80%", - "Sub-Sample K-Marginal Score": 972, + "Sub-Sample K-Marginal Score": 973, "Deidentified Data K-marginal score": 121, - "Absolute Diff. From Deidentified Data K-marginal Score": "851" + "Absolute Diff. From Deidentified Data K-marginal Score": "852" }, { "Sub-Sample Size": "90%", "Sub-Sample K-Marginal Score": 981, "Deidentified Data K-marginal score": 121, "Absolute Diff. From Deidentified Data K-marginal Score": "860" - }, - { - "Sub-Sample Size": "100%", - "Sub-Sample K-Marginal Score": 1000, - "Deidentified Data K-marginal score": 121, - "Absolute Diff. From Deidentified Data K-marginal Score": "879" } ], "type": "table", @@ -341,7 +363,7 @@ "Birmingham City (West)", "https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/" ], - "40% Target Subsample Baseline": 811, + "40% Target Subsample Baseline": 813, "Deidentified Data Score": 0 }, { @@ -350,7 +372,7 @@ "East Montana (Outside Billings City)", "https://censusreporter.org/profiles/79500US3000600-east-montana-outside-billings-city-puma-mt/" ], - "40% Target Subsample Baseline": 885, + "40% Target Subsample Baseline": 886, "Deidentified Data Score": 0 }, { @@ -359,7 +381,7 @@ "Central Region--Jackson City (East & Central)", "https://censusreporter.org/profiles/79500US2801100-central-region-jackson-city-east-central-puma-ms/" ], - "40% Target Subsample Baseline": 856, + "40% Target Subsample Baseline": 851, "Deidentified Data Score": 0 }, { @@ -377,7 +399,7 @@ "Boulder County (Central)--Boulder City", "https://censusreporter.org/profiles/79500US0800803-boulder-county-central-boulder-city-puma-co/" ], - "40% Target Subsample Baseline": 856, + "40% Target Subsample Baseline": 861, "Deidentified Data Score": 0 }, { @@ -386,7 +408,7 @@ "Santa Clara County (Southwest)--Cupertino, Saratoga Cities & Los Gatos Town", "https://censusreporter.org/profiles/79500US0608507-santa-clara-county-southwest-cupertino-saratoga-cities-los-gatos-town-puma-ca/" ], - "40% Target Subsample Baseline": 879, + "40% Target Subsample Baseline": 887, "Deidentified Data Score": 2 }, { @@ -395,7 +417,7 @@ "Las Vegas City (Southeast)", "https://censusreporter.org/profiles/79500US3200405-las-vegas-city-southeast-puma-nv/" ], - "40% Target Subsample Baseline": 857, + "40% Target Subsample Baseline": 853, "Deidentified Data Score": 5 }, { @@ -404,7 +426,7 @@ "San Francisco County (North & East)--North Beach & Chinatown", "https://censusreporter.org/profiles/79500US0607502-san-francisco-county-north-east-north-beach-chinatown-puma-ca/" ], - "40% Target Subsample Baseline": 854, + "40% Target Subsample Baseline": 850, "Deidentified Data Score": 6 }, { @@ -413,7 +435,7 @@ "Des Moines City", "https://censusreporter.org/profiles/79500US1901700-des-moines-city-puma-ia/" ], - "40% Target Subsample Baseline": 861, + "40% Target Subsample Baseline": 862, "Deidentified Data Score": 7 }, { @@ -422,7 +444,7 @@ "St. Louis City (North)", "https://censusreporter.org/profiles/79500US2901901-st-louis-city-north-puma-mo/" ], - "40% Target Subsample Baseline": 841, + "40% Target Subsample Baseline": 840, "Deidentified Data Score": 7 }, { @@ -431,7 +453,7 @@ "Chicago City (South)--South Shore, Hyde Park, Woodlawn, Grand Boulevard & Douglas", "https://censusreporter.org/profiles/79500US1703529-chicago-city-south-south-shore-hyde-park-woodlawn-grand-boulevard-douglas-puma-il/" ], - "40% Target Subsample Baseline": 866, + "40% Target Subsample Baseline": 873, "Deidentified Data Score": 8 }, { @@ -440,7 +462,7 @@ "NYC-Brooklyn Community District 17--East Flatbush, Farragut & Rugby", "https://censusreporter.org/profiles/79500US3604010-nyc-brooklyn-community-district-17-east-flatbush-farragut-rugby-puma-ny/" ], - "40% Target Subsample Baseline": 861, + "40% Target Subsample Baseline": 849, "Deidentified Data Score": 9 }, { @@ -449,7 +471,7 @@ "Cherokee, Sequoyah & Adair Counties", "https://censusreporter.org/profiles/79500US4000200-cherokee-sequoyah-adair-counties-puma-ok/" ], - "40% Target Subsample Baseline": 854, + "40% Target Subsample Baseline": 852, "Deidentified Data Score": 9 }, { @@ -458,7 +480,7 @@ "Montgomery County (South)--Bethesda, Potomac & North Bethesda", "https://censusreporter.org/profiles/79500US2401004-montgomery-county-south-bethesda-potomac-north-bethesda-puma-md/" ], - "40% Target Subsample Baseline": 902, + "40% Target Subsample Baseline": 901, "Deidentified Data Score": 9 }, { @@ -467,7 +489,7 @@ "Atlanta Regional Commission--Fulton County (Central)--Atlanta City (Central)", "https://censusreporter.org/profiles/79500US1304600-atlanta-regional-commission-fulton-county-central-atlanta-city-central-puma-ga/" ], - "40% Target Subsample Baseline": 857, + "40% Target Subsample Baseline": 853, "Deidentified Data Score": 10 }, { @@ -476,7 +498,7 @@ "Washtenaw County (East Central)--Ann Arbor City Area", "https://censusreporter.org/profiles/79500US2602702-washtenaw-county-east-central-ann-arbor-city-area-puma-mi/" ], - "40% Target Subsample Baseline": 844, + "40% Target Subsample Baseline": 852, "Deidentified Data Score": 14 }, { @@ -485,7 +507,7 @@ "NYC-Bronx Community District 1 & 2--Hunts Point, Longwood & Melrose", "https://censusreporter.org/profiles/79500US3603710-nyc-bronx-community-district-1-2-hunts-point-longwood-melrose-puma-ny/" ], - "40% Target Subsample Baseline": 834, + "40% Target Subsample Baseline": 833, "Deidentified Data Score": 15 }, { @@ -494,7 +516,7 @@ "West North Dakota--Minot City", "https://censusreporter.org/profiles/79500US3800100-west-north-dakota-minot-city-puma-nd/" ], - "40% Target Subsample Baseline": 885, + "40% Target Subsample Baseline": 889, "Deidentified Data Score": 25 }, { @@ -512,7 +534,7 @@ "Chicago City (South)--Auburn Gresham, Roseland, Chatham, Avalon Park & Burnside", "https://censusreporter.org/profiles/79500US1703531-chicago-city-south-auburn-gresham-roseland-chatham-avalon-park-burnside-puma-il/" ], - "40% Target Subsample Baseline": 841, + "40% Target Subsample Baseline": 828, "Deidentified Data Score": 116 } ], @@ -1457,7 +1479,7 @@ "-1": [ { "name": null, - "data": "This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you\u2019re using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes\u2013 the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.", + "data": "This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you're using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.", "type": "string", "dotted_break": false }, @@ -1524,7 +1546,7 @@ }, { "name": null, - "data": "The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP = \u201cN\u201d, individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. ", + "data": "The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP='N', individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. ", "type": "string", "dotted_break": false }, @@ -1556,6 +1578,12 @@ "metric_name": "Inconsistencies", "attachments": { "-1": [ + { + "name": null, + "data": "In real world tabular data, it's common for record features to have some deterministic, publicly known dependencies on each other: knowing someone's AGEP= 3 necessarily tells you something about their marital status, income and educational attainment. Different deidentification methods may be better or worse at automatically preserving these relationships. When they fail (ex: producing toddlers with PhDs) we say those records contain \"inconsistencies\". Our consistency check metric below is not exhaustive, we don't catch everything the way a production-grade system should, but this will give you a sense of how consistency is preserved for age, work and household features. Note that different deidentification approaches may do better or worse with different types of inconsistencies.", + "type": "string", + "dotted_break": false + }, { "name": "Summary", "data": { @@ -2563,12 +2591,12 @@ } }, { - "metric_name": "K-Marginal Score Breakdown", + "metric_name": "Worst Performing PUMAs Breakdown", "attachments": { "-1": [ { "name": null, - "data": "In the metrics above we\u2019ve considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score.", + "data": "In the metrics above we've considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score.", "type": "string", "dotted_break": false }, @@ -2587,7 +2615,7 @@ "Birmingham City (West)", "https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/" ], - "40% Target Subsample Baseline": 811, + "40% Target Subsample Baseline": 813, "Deidentified Data Score": 0 }, { @@ -2596,7 +2624,7 @@ "East Montana (Outside Billings City)", "https://censusreporter.org/profiles/79500US3000600-east-montana-outside-billings-city-puma-mt/" ], - "40% Target Subsample Baseline": 885, + "40% Target Subsample Baseline": 886, "Deidentified Data Score": 0 }, { @@ -2605,7 +2633,7 @@ "Central Region--Jackson City (East & Central)", "https://censusreporter.org/profiles/79500US2801100-central-region-jackson-city-east-central-puma-ms/" ], - "40% Target Subsample Baseline": 856, + "40% Target Subsample Baseline": 851, "Deidentified Data Score": 0 }, { @@ -2623,7 +2651,7 @@ "Boulder County (Central)--Boulder City", "https://censusreporter.org/profiles/79500US0800803-boulder-county-central-boulder-city-puma-co/" ], - "40% Target Subsample Baseline": 856, + "40% Target Subsample Baseline": 861, "Deidentified Data Score": 0 } ], @@ -2742,13 +2770,31 @@ "-1": [ { "name": null, - "data": "This is a count of unique records in the target data that were exactly reproduced in the deidentified data. Because these records were unique outliers in the target data, and they still appear unchanged in the deidentified data, they are potentially vulnerable to reidentification.", + "data": "Unique Exact Match (UEM) is a simple privacy metric that counts the percentage of singleton records in the target that are also present in the deidentified data; these uniquely identifiable individuals leaked through the deidentification process.", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "Below we also include an estimate of the feature space size. The feature space is the set of all possible record values given the selected target data and feature subset. For instance, if we had two features, Hat [cap, bonnet] and Color [green, blue, purple], our feature space would consist of 2 x 3 = 6 possible combinations (e.g. 'green cap', 'blue bonnet'). Note that feature spaces are based on the feature set, not on what records actually exist in the data. Purple bonnet is a possible combination in this feature space, but it's likely no one in the hypothetical data owns a purple bonnet (and the count of that record value would be 0).", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "As we add features to the feature set, we increase the size of the feature space, but we don't change the actual number of records in the data-- it's the same people, but now they're spread out more thinly across a wider set of possible record values. Large feature spaces will disperse populations very sparsely (most possible record values will have count 0 or 1) and as a result the data will contain very many uniquely identifiable records. Intuitively, once you know enough pieces of information about someone, everyone becomes very distinct from everyone else. This can pose a challenge for privacy. ", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "The Target Data Properties below provides an estimate of the feature space size (100 is used for continuous feature), along with the portion of the records in the ground truth target data that are unique (ie, they are the only person with that record value, they have a count of 1). The Deidentified Data Properties reports the percentage of those uniquely identifiable individuals that are still present in the deidentified data. Because they are unique, real records, they are potentially vulnerable to reidentification.", "type": "string", "dotted_break": false }, { "name": "Target Data Properties", - "data": "Feature space size (possible combinations): -Highlight-3,407,717,673,360,000-Highlight-
Number of unique records in Target Data: -Highlight-27159 (99.66%-Highlight-)", + "data": "Feature space size (possible combinations): -Highlight-5.178e+26-Highlight-
Number of unique records in Target Data: -Highlight-27159 (99.66%-Highlight-)", "type": "string", "dotted_break": false }, @@ -2779,7 +2825,7 @@ }, { "name": null, - "data": "OWN_RENT, RAC1P, SEX, PUMA, HISP, INDP_CAT, EDU, MSP", + "data": "HISP, OWN_RENT, PUMA, INDP_CAT, EDU, RAC1P, MSP, SEX", "type": "string", "dotted_break": false }, @@ -3395,6 +3441,12 @@ "type": "string", "dotted_break": false }, + { + "name": null, + "data": "There are a total of 271 possible codes for INDP, 269 of these codes appear in the Diverse Community Data Excerpts (233 in MA, 264 in Texas and National)", + "type": "string", + "dotted_break": false + }, { "name": "INDP_CAT: Industry categories", "data": [ diff --git a/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_05-19-2023T18.01.12/univariate/AGEP.jpg b/sdnist/report/sample-reports/report_adsgan_ZhaozhiQian_1_national2019_06-19-2023T20.33.47/univariate/AGEP.jpg similarity index 100% rename from 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a/sdnist/report/sample-reports/report_cart_cf21_na2019_05-19-2023T18.01.12/k_marginal_breakdown/univariate/indp_indp_cat_8.jpg b/sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/k_marginal_breakdown/univariate/indp_indp_cat_8.jpg similarity index 100% rename from sdnist/report/sample-reports/report_cart_cf21_na2019_05-19-2023T18.01.12/k_marginal_breakdown/univariate/indp_indp_cat_8.jpg rename to sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/k_marginal_breakdown/univariate/indp_indp_cat_8.jpg diff --git a/sdnist/report/sample-reports/report_cart_cf21_na2019_05-19-2023T18.01.12/k_marginal_breakdown/univariate/indp_indp_cat_9.jpg b/sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/k_marginal_breakdown/univariate/indp_indp_cat_9.jpg similarity index 100% rename from sdnist/report/sample-reports/report_cart_cf21_na2019_05-19-2023T18.01.12/k_marginal_breakdown/univariate/indp_indp_cat_9.jpg rename to sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/k_marginal_breakdown/univariate/indp_indp_cat_9.jpg diff --git a/sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv b/sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv new file mode 100644 index 0000000..829edc2 --- /dev/null +++ b/sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv @@ -0,0 +1,6 @@ +,PUMA,40% Target Subsample Baseline,Deidentified Data Score +0,01-01301,826,814 +1,36-03710,857,819 +2,17-03531,847,836 +3,32-00405,872,838 +4,29-01901,854,842 diff --git a/sdnist/report/sample-reports/report_cart_cf21_na2019_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv b/sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv similarity index 84% rename from sdnist/report/sample-reports/report_cart_cf21_na2019_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv rename to sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv index 33903ea..290e145 100644 --- a/sdnist/report/sample-reports/report_cart_cf21_na2019_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv +++ b/sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv @@ -1,21 +1,21 @@ ,PUMA,40% Target Subsample Baseline,Deidentified Data Score -0,"['01-01301', 'Birmingham City (West)', 'https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/']",835,814 -1,"['36-03710', 'NYC-Bronx Community District 1 & 2--Hunts Point, Longwood & Melrose', 'https://censusreporter.org/profiles/79500US3603710-nyc-bronx-community-district-1-2-hunts-point-longwood-melrose-puma-ny/']",855,819 -2,"['17-03531', 'Chicago City (South)--Auburn Gresham, Roseland, Chatham, Avalon Park & Burnside', 'https://censusreporter.org/profiles/79500US1703531-chicago-city-south-auburn-gresham-roseland-chatham-avalon-park-burnside-puma-il/']",851,836 -3,"['32-00405', 'Las Vegas City (Southeast)', 'https://censusreporter.org/profiles/79500US3200405-las-vegas-city-southeast-puma-nv/']",869,838 -4,"['29-01901', 'St. Louis City (North)', 'https://censusreporter.org/profiles/79500US2901901-st-louis-city-north-puma-mo/']",851,842 -5,"['36-04010', 'NYC-Brooklyn Community District 17--East Flatbush, Farragut & Rugby', 'https://censusreporter.org/profiles/79500US3604010-nyc-brooklyn-community-district-17-east-flatbush-farragut-rugby-puma-ny/']",871,843 +0,"['01-01301', 'Birmingham City (West)', 'https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/']",826,814 +1,"['36-03710', 'NYC-Bronx Community District 1 & 2--Hunts Point, Longwood & Melrose', 'https://censusreporter.org/profiles/79500US3603710-nyc-bronx-community-district-1-2-hunts-point-longwood-melrose-puma-ny/']",857,819 +2,"['17-03531', 'Chicago City (South)--Auburn Gresham, Roseland, Chatham, Avalon Park & Burnside', 'https://censusreporter.org/profiles/79500US1703531-chicago-city-south-auburn-gresham-roseland-chatham-avalon-park-burnside-puma-il/']",847,836 +3,"['32-00405', 'Las Vegas City (Southeast)', 'https://censusreporter.org/profiles/79500US3200405-las-vegas-city-southeast-puma-nv/']",872,838 +4,"['29-01901', 'St. Louis City (North)', 'https://censusreporter.org/profiles/79500US2901901-st-louis-city-north-puma-mo/']",854,842 +5,"['36-04010', 'NYC-Brooklyn Community District 17--East Flatbush, Farragut & Rugby', 'https://censusreporter.org/profiles/79500US3604010-nyc-brooklyn-community-district-17-east-flatbush-farragut-rugby-puma-ny/']",869,843 6,"['17-03529', 'Chicago City (South)--South Shore, Hyde Park, Woodlawn, Grand Boulevard & Douglas', 'https://censusreporter.org/profiles/79500US1703529-chicago-city-south-south-shore-hyde-park-woodlawn-grand-boulevard-douglas-puma-il/']",886,849 -7,"['26-02702', 'Washtenaw County (East Central)--Ann Arbor City Area', 'https://censusreporter.org/profiles/79500US2602702-washtenaw-county-east-central-ann-arbor-city-area-puma-mi/']",862,852 -8,"['40-00200', 'Cherokee, Sequoyah & Adair Counties', 'https://censusreporter.org/profiles/79500US4000200-cherokee-sequoyah-adair-counties-puma-ok/']",871,852 -9,"['28-01100', 'Central Region--Jackson City (East & Central)', 'https://censusreporter.org/profiles/79500US2801100-central-region-jackson-city-east-central-puma-ms/']",867,863 -10,"['06-07502', 'San Francisco County (North & East)--North Beach & Chinatown', 'https://censusreporter.org/profiles/79500US0607502-san-francisco-county-north-east-north-beach-chinatown-puma-ca/']",875,864 -11,"['13-04600', 'Atlanta Regional Commission--Fulton County (Central)--Atlanta City (Central)', 'https://censusreporter.org/profiles/79500US1304600-atlanta-regional-commission-fulton-county-central-atlanta-city-central-puma-ga/']",881,864 -12,"['19-01700', 'Des Moines City', 'https://censusreporter.org/profiles/79500US1901700-des-moines-city-puma-ia/']",880,870 -13,"['51-01301', 'Arlington County (North)', 'https://censusreporter.org/profiles/79500US5101301-arlington-county-north-puma-va/']",890,877 +7,"['26-02702', 'Washtenaw County (East Central)--Ann Arbor City Area', 'https://censusreporter.org/profiles/79500US2602702-washtenaw-county-east-central-ann-arbor-city-area-puma-mi/']",867,852 +8,"['40-00200', 'Cherokee, Sequoyah & Adair Counties', 'https://censusreporter.org/profiles/79500US4000200-cherokee-sequoyah-adair-counties-puma-ok/']",867,852 +9,"['28-01100', 'Central Region--Jackson City (East & Central)', 'https://censusreporter.org/profiles/79500US2801100-central-region-jackson-city-east-central-puma-ms/']",863,863 +10,"['06-07502', 'San Francisco County (North & East)--North Beach & Chinatown', 'https://censusreporter.org/profiles/79500US0607502-san-francisco-county-north-east-north-beach-chinatown-puma-ca/']",871,864 +11,"['13-04600', 'Atlanta Regional Commission--Fulton County (Central)--Atlanta City (Central)', 'https://censusreporter.org/profiles/79500US1304600-atlanta-regional-commission-fulton-county-central-atlanta-city-central-puma-ga/']",868,864 +12,"['19-01700', 'Des Moines City', 'https://censusreporter.org/profiles/79500US1901700-des-moines-city-puma-ia/']",869,870 +13,"['51-01301', 'Arlington County (North)', 'https://censusreporter.org/profiles/79500US5101301-arlington-county-north-puma-va/']",888,877 14,"['08-00803', 'Boulder County (Central)--Boulder City', 'https://censusreporter.org/profiles/79500US0800803-boulder-county-central-boulder-city-puma-co/']",884,880 15,"['38-00100', 'West North Dakota--Minot City', 'https://censusreporter.org/profiles/79500US3800100-west-north-dakota-minot-city-puma-nd/']",902,892 -16,"['30-00600', 'East Montana (Outside Billings City)', 'https://censusreporter.org/profiles/79500US3000600-east-montana-outside-billings-city-puma-mt/']",901,893 -17,"['51-51255', 'Alexandria City', 'https://censusreporter.org/profiles/79500US5151255-alexandria-city-puma-va/']",888,894 -18,"['06-08507', 'Santa Clara County (Southwest)--Cupertino, Saratoga Cities & Los Gatos Town', 'https://censusreporter.org/profiles/79500US0608507-santa-clara-county-southwest-cupertino-saratoga-cities-los-gatos-town-puma-ca/']",896,904 -19,"['24-01004', 'Montgomery County (South)--Bethesda, Potomac & North Bethesda', 'https://censusreporter.org/profiles/79500US2401004-montgomery-county-south-bethesda-potomac-north-bethesda-puma-md/']",917,920 +16,"['30-00600', 'East Montana (Outside Billings City)', 'https://censusreporter.org/profiles/79500US3000600-east-montana-outside-billings-city-puma-mt/']",895,893 +17,"['51-51255', 'Alexandria City', 'https://censusreporter.org/profiles/79500US5151255-alexandria-city-puma-va/']",887,894 +18,"['06-08507', 'Santa Clara County (Southwest)--Cupertino, Saratoga Cities & Los Gatos Town', 'https://censusreporter.org/profiles/79500US0608507-santa-clara-county-southwest-cupertino-saratoga-cities-los-gatos-town-puma-ca/']",900,904 +19,"['24-01004', 'Montgomery County (South)--Bethesda, Potomac & North Bethesda', 'https://censusreporter.org/profiles/79500US2401004-montgomery-county-south-bethesda-potomac-north-bethesda-puma-md/']",913,920 diff --git a/sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/k_marginal_synopsys/subsample_error_comparison.csv b/sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/k_marginal_synopsys/subsample_error_comparison.csv new file mode 100644 index 0000000..7861913 --- /dev/null +++ b/sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/k_marginal_synopsys/subsample_error_comparison.csv @@ -0,0 +1,12 @@ +,Sub-Sample Size,Sub-Sample K-Marginal Score,Deidentified Data K-marginal score,Absolute Diff. 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sdnist/report/sample-reports/report_cart_cf21_na2019_05-19-2023T18.01.12/linear_regression/white_women/target_deidentified_counts_difference.csv rename to sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/linear_regression/white_women/target_deidentified_counts_difference.csv diff --git a/sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/pca/components_eigenvector.csv b/sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/pca/components_eigenvector.csv new file mode 100644 index 0000000..62a0adc --- /dev/null +++ b/sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/pca/components_eigenvector.csv @@ -0,0 +1,6 @@ +,AGEP,DEAR,DEYE,DPHY,DREM,DVET,EDU,HISP,HOUSING_TYPE,INDP,INDP_CAT,MSP,NOC,NPF,OWN_RENT,PINCP,PINCP_DECILE,POVPIP,PUMA,RAC1P,SEX 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a/sdnist/report/sample-reports/report_cart_cf21_na2019_05-19-2023T18.01.12/propensity/propensity_distribution.csv +++ b/sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/propensity/propensity_distribution.csv @@ -1,5 +1,5 @@ ,Target samples,Deid. samples -0,40,0 +0,41,0 1,0,0 2,0,0 3,0,0 @@ -66,7 +66,7 @@ 64,0,0 65,7,13 66,0,0 -67,3,6 +67,1,2 68,0,0 69,0,0 70,0,0 @@ -79,7 +79,7 @@ 77,0,0 78,0,0 79,0,0 -80,0,0 +80,1,4 81,0,0 82,0,0 83,2,10 diff --git a/sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/propensity/propensity_distribution.jpg b/sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/propensity/propensity_distribution.jpg new file mode 100644 index 0000000..09519c4 Binary files /dev/null and b/sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/propensity/propensity_distribution.jpg differ diff --git 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Data Evaluation Report


-

Report created on: May 19, 2023 18:06:13

+

Report created on: June 19, 2023 20:39:45

Created with - SDNIST v2.2.1 + SDNIST v2.3.0

@@ -230,7 +230,52 @@

- Library + Target Dataset + + + + national2019 + + + + + + + + + + + Variant Label + + + + maxfaclevels: 300 + + + + + + + + + + + Algorithm Type + + + + stat model + + + + + + + + + + + Library Name @@ -245,7 +290,7 @@

- Feature Set + Feature Set Name @@ -260,11 +305,11 @@

- Target Dataset + Privacy Category - national2019 + non_dp @@ -275,11 +320,26 @@

- Variant Label + Deid Data Id - maxfaclevels: 300 + a4e947b700c619f808cb68ec50472a503b5c337c + + + + + + + + + + + Features List + + + + PUMA, AGEP, SEX, MSP, HISP, RAC1P, NOC, NPF, HOUSING_TYPE, OWN_RENT, INDP, INDP_CAT, EDU, PINCP, PINCP_DECILE, POVPIP, DVET, DREM, DPHY, DEYE, DEAR @@ -290,11 +350,56 @@

- Privacy + Privacy Label Detail - Synthetic Data (Non-differentially Private) + Non-differentially private synthetic data. Produced using a fully conditional multilple imputation algorithm comprised of a sequence of decision trees trained on the target data. Each feature value in a given record is synthesized one at a time: the values of the preceding synthesized features are fed into the decision tree trained to predict the current feature. + + + + + + + + + + + Submission Timestamp + + + + 5/20/2023 00:00:00 + + + + + + + + + + + Team + + + + CRC + + + + + + + + + + + Research Papers + + + + https://doi.org/10.18637/jss.v074.i11 @@ -1188,6 +1293,52 @@

+ + + + 1% + + + + 617 + + + + 935 + + + + 318 + + + + + + + + + + + 5% + + + + 790 + + + + 935 + + + + 145 + + + + + + + @@ -1195,7 +1346,7 @@

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- 15 + 16 @@ -1336,7 +1487,7 @@

- 967 + 969 @@ -1344,7 +1495,7 @@

- 32 + 34 @@ -1395,29 +1546,6 @@

- - - - - - - 100% - - - - 1000 - - - - 935 - - - - 65 - - - -

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- 890 + 888 @@ -1869,7 +1997,7 @@

- 901 + 895 @@ -1891,7 +2019,7 @@

- 888 + 887 @@ -1913,7 +2041,7 @@

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- 917 + 913 @@ -4975,7 +5103,7 @@

- This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you’re using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes– the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive. + This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you're using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.
@@ -5258,7 +5386,7 @@

- The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP = “N”, individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. + The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP='N', individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape.
@@ -5368,6 +5496,34 @@

+
+ +
+ + + + +
+ In real world tabular data, it's common for record features to have some deterministic, publicly known dependencies on each other: knowing someone's AGEP= 3 necessarily tells you something about their marital status, income and educational attainment. Different deidentification methods may be better or worse at automatically preserving these relationships. When they fail (ex: producing toddlers with PhDs) we say those records contain "inconsistencies". Our consistency check metric below is not exhaustive, we don't catch everything the way a production-grade system should, but this will give you a sense of how consistency is preserved for age, work and household features. Note that different deidentification approaches may do better or worse with different types of inconsistencies. +
+ + + +
+ + + + + + + + + + + + + +

@@ -6665,7 +6821,7 @@

- K-Marginal Score Breakdown: + Worst Performing PUMAs Breakdown:

@@ -6689,7 +6845,7 @@

- In the metrics above we’ve considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score. + In the metrics above we've considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score.
@@ -6788,7 +6944,7 @@

- 835 + 826 @@ -6810,7 +6966,7 @@

- 855 + 857 @@ -6832,7 +6988,7 @@

- 851 + 847 @@ -6854,7 +7010,7 @@

- 869 + 872 @@ -6876,7 +7032,7 @@

- 851 + 854 @@ -7356,6 +7512,7 @@

Privacy Evaluation

+

@@ -7383,7 +7540,91 @@

- This is a count of unique records in the target data that were exactly reproduced in the deidentified data. Because these records were unique outliers in the target data, and they still appear unchanged in the deidentified data, they are potentially vulnerable to reidentification. + Unique Exact Match (UEM) is a simple privacy metric that counts the percentage of singleton records in the target that are also present in the deidentified data; these uniquely identifiable individuals leaked through the deidentification process. +
+ + + +

+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ Below we also include an estimate of the feature space size. The feature space is the set of all possible record values given the selected target data and feature subset. For instance, if we had two features, Hat [cap, bonnet] and Color [green, blue, purple], our feature space would consist of 2 x 3 = 6 possible combinations (e.g. 'green cap', 'blue bonnet'). Note that feature spaces are based on the feature set, not on what records actually exist in the data. Purple bonnet is a possible combination in this feature space, but it's likely no one in the hypothetical data owns a purple bonnet (and the count of that record value would be 0). +
+ + + +
+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ As we add features to the feature set, we increase the size of the feature space, but we don't change the actual number of records in the data-- it's the same people, but now they're spread out more thinly across a wider set of possible record values. Large feature spaces will disperse populations very sparsely (most possible record values will have count 0 or 1) and as a result the data will contain very many uniquely identifiable records. Intuitively, once you know enough pieces of information about someone, everyone becomes very distinct from everyone else. This can pose a challenge for privacy. +
+ + + +
+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ The Target Data Properties below provides an estimate of the feature space size (100 is used for continuous feature), along with the portion of the records in the ground truth target data that are unique (ie, they are the only person with that record value, they have a count of 1). The Deidentified Data Properties reports the percentage of those uniquely identifiable individuals that are still present in the deidentified data. Because they are unique, real records, they are potentially vulnerable to reidentification.
@@ -7427,7 +7668,7 @@

- 3,407,717,673,360,000 + 2.589e+21 @@ -7489,7 +7730,7 @@

- 654 (2.4%) + 654 (2.54%) @@ -7516,6 +7757,12 @@

+
+
+
+
+ +

@@ -7606,7 +7853,7 @@

- MSP, EDU, RAC1P, SEX, PUMA, OWN_RENT, HISP, INDP_CAT + EDU, SEX, MSP, PUMA, RAC1P, INDP_CAT, OWN_RENT, HISP
@@ -10480,6 +10727,34 @@

+
+ +
+ + + + +
+ There are a total of 271 possible codes for INDP, 269 of these codes appear in the Diverse Community Data Excerpts (233 in MA, 264 in Texas and National) +
+ + + +
+ + + + + + + + + + + + + +

diff --git a/sdnist/report/sample-reports/report_cart_cf21_na2019_05-19-2023T18.01.12/ui.json b/sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/ui.json similarity index 94% rename from sdnist/report/sample-reports/report_cart_cf21_na2019_05-19-2023T18.01.12/ui.json rename to sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/ui.json index e1823c8..449b14a 100644 --- a/sdnist/report/sample-reports/report_cart_cf21_na2019_05-19-2023T18.01.12/ui.json +++ b/sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/ui.json @@ -1,6 +1,6 @@ { - "Created on": "May 19, 2023 18:06:13", - "version": "2.2.1", + "Created on": "June 19, 2023 20:39:45", + "version": "2.3.0", "data_description": { "target": [ { @@ -43,24 +43,52 @@ "Label Value": "cart" }, { - "Label Name": "Library", + "Label Name": "Target Dataset", + "Label Value": "national2019" + }, + { + "Label Name": "Variant Label", + "Label Value": "maxfaclevels: 300" + }, + { + "Label Name": "Algorithm Type", + "Label Value": "stat model" + }, + { + "Label Name": "Library Name", "Label Value": "rsynthpop" }, { - "Label Name": "Feature Set", + "Label Name": "Feature Set Name", "Label Value": "custom-features-21" }, { - "Label Name": "Target Dataset", - "Label Value": "national2019" + "Label Name": "Privacy Category", + "Label Value": "non_dp" }, { - "Label Name": "Variant Label", - "Label Value": "maxfaclevels: 300" + "Label Name": "Deid Data Id", + "Label Value": "a4e947b700c619f808cb68ec50472a503b5c337c" + }, + { + "Label Name": "Features List", + "Label Value": "PUMA, AGEP, SEX, MSP, HISP, RAC1P, NOC, NPF, HOUSING_TYPE, OWN_RENT, INDP, INDP_CAT, EDU, PINCP, PINCP_DECILE, POVPIP, DVET, DREM, DPHY, DEYE, DEAR" + }, + { + "Label Name": "Privacy Label Detail", + "Label Value": "Non-differentially private synthetic data. Produced using a fully conditional multilple imputation algorithm comprised of a sequence of decision trees trained on the target data. Each feature value in a given record is synthesized one at a time: the values of the preceding synthesized features are fed into the decision tree trained to predict the current feature. " + }, + { + "Label Name": "Submission Timestamp", + "Label Value": "5/20/2023 00:00:00" + }, + { + "Label Name": "Team", + "Label Value": "CRC" }, { - "Label Name": "Privacy", - "Label Value": "Synthetic Data (Non-differentially Private)" + "Label Name": "Research Papers", + "Label Value": "https://doi.org/10.18637/jss.v074.i11" } ], "validations": [] @@ -227,17 +255,29 @@ { "name": null, "data": [ + { + "Sub-Sample Size": "1%", + "Sub-Sample K-Marginal Score": 617, + "Deidentified Data K-marginal score": 935, + "Absolute Diff. From Deidentified Data K-marginal Score": "318" + }, + { + "Sub-Sample Size": "5%", + "Sub-Sample K-Marginal Score": 790, + "Deidentified Data K-marginal score": 935, + "Absolute Diff. From Deidentified Data K-marginal Score": "145" + }, { "Sub-Sample Size": "10%", - "Sub-Sample K-Marginal Score": 858, + "Sub-Sample K-Marginal Score": 857, "Deidentified Data K-marginal score": 935, - "Absolute Diff. From Deidentified Data K-marginal Score": "77" + "Absolute Diff. From Deidentified Data K-marginal Score": "78" }, { "Sub-Sample Size": "20%", - "Sub-Sample K-Marginal Score": 906, + "Sub-Sample K-Marginal Score": 907, "Deidentified Data K-marginal score": 935, - "Absolute Diff. From Deidentified Data K-marginal Score": "29" + "Absolute Diff. From Deidentified Data K-marginal Score": "28" }, { "Sub-Sample Size": "30%", @@ -254,9 +294,9 @@ }, { "Sub-Sample Size": "50%", - "Sub-Sample K-Marginal Score": 950, + "Sub-Sample K-Marginal Score": 951, "Deidentified Data K-marginal score": 935, - "Absolute Diff. From Deidentified Data K-marginal Score": "15" + "Absolute Diff. From Deidentified Data K-marginal Score": "16" }, { "Sub-Sample Size": "60%", @@ -266,9 +306,9 @@ }, { "Sub-Sample Size": "70%", - "Sub-Sample K-Marginal Score": 967, + "Sub-Sample K-Marginal Score": 969, "Deidentified Data K-marginal score": 935, - "Absolute Diff. From Deidentified Data K-marginal Score": "32" + "Absolute Diff. From Deidentified Data K-marginal Score": "34" }, { "Sub-Sample Size": "80%", @@ -281,12 +321,6 @@ "Sub-Sample K-Marginal Score": 984, "Deidentified Data K-marginal score": 935, "Absolute Diff. From Deidentified Data K-marginal Score": "49" - }, - { - "Sub-Sample Size": "100%", - "Sub-Sample K-Marginal Score": 1000, - "Deidentified Data K-marginal score": 935, - "Absolute Diff. From Deidentified Data K-marginal Score": "65" } ], "type": "table", @@ -307,7 +341,7 @@ "Birmingham City (West)", "https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/" ], - "40% Target Subsample Baseline": 835, + "40% Target Subsample Baseline": 826, "Deidentified Data Score": 814 }, { @@ -316,7 +350,7 @@ "NYC-Bronx Community District 1 & 2--Hunts Point, Longwood & Melrose", "https://censusreporter.org/profiles/79500US3603710-nyc-bronx-community-district-1-2-hunts-point-longwood-melrose-puma-ny/" ], - "40% Target Subsample Baseline": 855, + "40% Target Subsample Baseline": 857, "Deidentified Data Score": 819 }, { @@ -325,7 +359,7 @@ "Chicago City (South)--Auburn Gresham, Roseland, Chatham, Avalon Park & Burnside", "https://censusreporter.org/profiles/79500US1703531-chicago-city-south-auburn-gresham-roseland-chatham-avalon-park-burnside-puma-il/" ], - "40% Target Subsample Baseline": 851, + "40% Target Subsample Baseline": 847, "Deidentified Data Score": 836 }, { @@ -334,7 +368,7 @@ "Las Vegas City (Southeast)", "https://censusreporter.org/profiles/79500US3200405-las-vegas-city-southeast-puma-nv/" ], - "40% Target Subsample Baseline": 869, + "40% Target Subsample Baseline": 872, "Deidentified Data Score": 838 }, { @@ -343,7 +377,7 @@ "St. Louis City (North)", "https://censusreporter.org/profiles/79500US2901901-st-louis-city-north-puma-mo/" ], - "40% Target Subsample Baseline": 851, + "40% Target Subsample Baseline": 854, "Deidentified Data Score": 842 }, { @@ -352,7 +386,7 @@ "NYC-Brooklyn Community District 17--East Flatbush, Farragut & Rugby", "https://censusreporter.org/profiles/79500US3604010-nyc-brooklyn-community-district-17-east-flatbush-farragut-rugby-puma-ny/" ], - "40% Target Subsample Baseline": 871, + "40% Target Subsample Baseline": 869, "Deidentified Data Score": 843 }, { @@ -370,7 +404,7 @@ "Washtenaw County (East Central)--Ann Arbor City Area", "https://censusreporter.org/profiles/79500US2602702-washtenaw-county-east-central-ann-arbor-city-area-puma-mi/" ], - "40% Target Subsample Baseline": 862, + "40% Target Subsample Baseline": 867, "Deidentified Data Score": 852 }, { @@ -379,7 +413,7 @@ "Cherokee, Sequoyah & Adair Counties", "https://censusreporter.org/profiles/79500US4000200-cherokee-sequoyah-adair-counties-puma-ok/" ], - "40% Target Subsample Baseline": 871, + "40% Target Subsample Baseline": 867, "Deidentified Data Score": 852 }, { @@ -388,7 +422,7 @@ "Central Region--Jackson City (East & Central)", "https://censusreporter.org/profiles/79500US2801100-central-region-jackson-city-east-central-puma-ms/" ], - "40% Target Subsample Baseline": 867, + "40% Target Subsample Baseline": 863, "Deidentified Data Score": 863 }, { @@ -397,7 +431,7 @@ "San Francisco County (North & East)--North Beach & Chinatown", "https://censusreporter.org/profiles/79500US0607502-san-francisco-county-north-east-north-beach-chinatown-puma-ca/" ], - "40% Target Subsample Baseline": 875, + "40% Target Subsample Baseline": 871, "Deidentified Data Score": 864 }, { @@ -406,7 +440,7 @@ "Atlanta Regional Commission--Fulton County (Central)--Atlanta City (Central)", "https://censusreporter.org/profiles/79500US1304600-atlanta-regional-commission-fulton-county-central-atlanta-city-central-puma-ga/" ], - "40% Target Subsample Baseline": 881, + "40% Target Subsample Baseline": 868, "Deidentified Data Score": 864 }, { @@ -415,7 +449,7 @@ "Des Moines City", "https://censusreporter.org/profiles/79500US1901700-des-moines-city-puma-ia/" ], - "40% Target Subsample Baseline": 880, + "40% Target Subsample Baseline": 869, "Deidentified Data Score": 870 }, { @@ -424,7 +458,7 @@ "Arlington County (North)", "https://censusreporter.org/profiles/79500US5101301-arlington-county-north-puma-va/" ], - "40% Target Subsample Baseline": 890, + "40% Target Subsample Baseline": 888, "Deidentified Data Score": 877 }, { @@ -451,7 +485,7 @@ "East Montana (Outside Billings City)", "https://censusreporter.org/profiles/79500US3000600-east-montana-outside-billings-city-puma-mt/" ], - "40% Target Subsample Baseline": 901, + "40% Target Subsample Baseline": 895, "Deidentified Data Score": 893 }, { @@ -460,7 +494,7 @@ "Alexandria City", "https://censusreporter.org/profiles/79500US5151255-alexandria-city-puma-va/" ], - "40% Target Subsample Baseline": 888, + "40% Target Subsample Baseline": 887, "Deidentified Data Score": 894 }, { @@ -469,7 +503,7 @@ "Santa Clara County (Southwest)--Cupertino, Saratoga Cities & Los Gatos Town", "https://censusreporter.org/profiles/79500US0608507-santa-clara-county-southwest-cupertino-saratoga-cities-los-gatos-town-puma-ca/" ], - "40% Target Subsample Baseline": 896, + "40% Target Subsample Baseline": 900, "Deidentified Data Score": 904 }, { @@ -478,7 +512,7 @@ "Montgomery County (South)--Bethesda, Potomac & North Bethesda", "https://censusreporter.org/profiles/79500US2401004-montgomery-county-south-bethesda-potomac-north-bethesda-puma-md/" ], - "40% Target Subsample Baseline": 917, + "40% Target Subsample Baseline": 913, "Deidentified Data Score": 920 } ], @@ -1372,7 +1406,7 @@ "-1": [ { "name": null, - "data": "This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you\u2019re using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes\u2013 the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.", + "data": "This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you're using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.", "type": "string", "dotted_break": false }, @@ -1439,7 +1473,7 @@ }, { "name": null, - "data": "The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP = \u201cN\u201d, individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. ", + "data": "The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP='N', individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. ", "type": "string", "dotted_break": false }, @@ -1471,6 +1505,12 @@ "metric_name": "Inconsistencies", "attachments": { "-1": [ + { + "name": null, + "data": "In real world tabular data, it's common for record features to have some deterministic, publicly known dependencies on each other: knowing someone's AGEP= 3 necessarily tells you something about their marital status, income and educational attainment. Different deidentification methods may be better or worse at automatically preserving these relationships. When they fail (ex: producing toddlers with PhDs) we say those records contain \"inconsistencies\". Our consistency check metric below is not exhaustive, we don't catch everything the way a production-grade system should, but this will give you a sense of how consistency is preserved for age, work and household features. Note that different deidentification approaches may do better or worse with different types of inconsistencies.", + "type": "string", + "dotted_break": false + }, { "name": "Summary", "data": { @@ -1632,12 +1672,12 @@ } }, { - "metric_name": "K-Marginal Score Breakdown", + "metric_name": "Worst Performing PUMAs Breakdown", "attachments": { "-1": [ { "name": null, - "data": "In the metrics above we\u2019ve considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score.", + "data": "In the metrics above we've considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score.", "type": "string", "dotted_break": false }, @@ -1656,7 +1696,7 @@ "Birmingham City (West)", "https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/" ], - "40% Target Subsample Baseline": 835, + "40% Target Subsample Baseline": 826, "Deidentified Data Score": 814 }, { @@ -1665,7 +1705,7 @@ "NYC-Bronx Community District 1 & 2--Hunts Point, Longwood & Melrose", "https://censusreporter.org/profiles/79500US3603710-nyc-bronx-community-district-1-2-hunts-point-longwood-melrose-puma-ny/" ], - "40% Target Subsample Baseline": 855, + "40% Target Subsample Baseline": 857, "Deidentified Data Score": 819 }, { @@ -1674,7 +1714,7 @@ "Chicago City (South)--Auburn Gresham, Roseland, Chatham, Avalon Park & Burnside", "https://censusreporter.org/profiles/79500US1703531-chicago-city-south-auburn-gresham-roseland-chatham-avalon-park-burnside-puma-il/" ], - "40% Target Subsample Baseline": 851, + "40% Target Subsample Baseline": 847, "Deidentified Data Score": 836 }, { @@ -1683,7 +1723,7 @@ "Las Vegas City (Southeast)", "https://censusreporter.org/profiles/79500US3200405-las-vegas-city-southeast-puma-nv/" ], - "40% Target Subsample Baseline": 869, + "40% Target Subsample Baseline": 872, "Deidentified Data Score": 838 }, { @@ -1692,7 +1732,7 @@ "St. Louis City (North)", "https://censusreporter.org/profiles/79500US2901901-st-louis-city-north-puma-mo/" ], - "40% Target Subsample Baseline": 851, + "40% Target Subsample Baseline": 854, "Deidentified Data Score": 842 } ], @@ -1817,19 +1857,37 @@ "-1": [ { "name": null, - "data": "This is a count of unique records in the target data that were exactly reproduced in the deidentified data. Because these records were unique outliers in the target data, and they still appear unchanged in the deidentified data, they are potentially vulnerable to reidentification.", + "data": "Unique Exact Match (UEM) is a simple privacy metric that counts the percentage of singleton records in the target that are also present in the deidentified data; these uniquely identifiable individuals leaked through the deidentification process.", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "Below we also include an estimate of the feature space size. The feature space is the set of all possible record values given the selected target data and feature subset. For instance, if we had two features, Hat [cap, bonnet] and Color [green, blue, purple], our feature space would consist of 2 x 3 = 6 possible combinations (e.g. 'green cap', 'blue bonnet'). Note that feature spaces are based on the feature set, not on what records actually exist in the data. Purple bonnet is a possible combination in this feature space, but it's likely no one in the hypothetical data owns a purple bonnet (and the count of that record value would be 0).", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "As we add features to the feature set, we increase the size of the feature space, but we don't change the actual number of records in the data-- it's the same people, but now they're spread out more thinly across a wider set of possible record values. Large feature spaces will disperse populations very sparsely (most possible record values will have count 0 or 1) and as a result the data will contain very many uniquely identifiable records. Intuitively, once you know enough pieces of information about someone, everyone becomes very distinct from everyone else. This can pose a challenge for privacy. ", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "The Target Data Properties below provides an estimate of the feature space size (100 is used for continuous feature), along with the portion of the records in the ground truth target data that are unique (ie, they are the only person with that record value, they have a count of 1). The Deidentified Data Properties reports the percentage of those uniquely identifiable individuals that are still present in the deidentified data. Because they are unique, real records, they are potentially vulnerable to reidentification.", "type": "string", "dotted_break": false }, { "name": "Target Data Properties", - "data": "Feature space size (possible combinations): -Highlight-3,407,717,673,360,000-Highlight-
Number of unique records in Target Data: -Highlight-25774 (94.57%-Highlight-)", + "data": "Feature space size (possible combinations): -Highlight-2.589e+21-Highlight-
Number of unique records in Target Data: -Highlight-25774 (94.57%-Highlight-)", "type": "string", "dotted_break": false }, { "name": "Deidentified Data Properties", - "data": "Number of unique Target Data records exactly matched in Deid. Data: -Highlight-654 (2.4%)-Highlight-", + "data": "Number of unique Target Data records exactly matched in Deid. Data: -Highlight-654 (2.54%)-Highlight-", "type": "string", "dotted_break": false } @@ -1854,7 +1912,7 @@ }, { "name": null, - "data": "MSP, EDU, RAC1P, SEX, PUMA, OWN_RENT, HISP, INDP_CAT", + "data": "EDU, SEX, MSP, PUMA, RAC1P, INDP_CAT, OWN_RENT, HISP", "type": "string", "dotted_break": false }, @@ -2470,6 +2528,12 @@ "type": "string", "dotted_break": false }, + { + "name": null, + "data": "There are a total of 271 possible codes for INDP, 269 of these codes appear in the Diverse Community Data Excerpts (233 in MA, 264 in Texas and National)", + "type": "string", + "dotted_break": false + }, { "name": "INDP_CAT: Industry categories", "data": [ diff --git a/sdnist/report/sample-reports/report_cart_cf21_na2019_05-19-2023T18.01.12/univariate/AGEP.jpg b/sdnist/report/sample-reports/report_cart_cf21_na2019_national2019_06-19-2023T20.33.47/univariate/AGEP.jpg similarity index 100% rename from sdnist/report/sample-reports/report_cart_cf21_na2019_05-19-2023T18.01.12/univariate/AGEP.jpg 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similarity index 100% rename from sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_05-19-2023T18.01.12/inconsistencies/work/house_OWN_RENT_example.csv rename to sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_national2019_06-19-2023T20.33.47/inconsistencies/work/house_OWN_RENT_example.csv diff --git a/sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_national2019_06-19-2023T20.33.47/k_marginal_synopsys/subsample_error_comparison.csv b/sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_national2019_06-19-2023T20.33.47/k_marginal_synopsys/subsample_error_comparison.csv new file mode 100644 index 0000000..7920285 --- /dev/null +++ b/sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_national2019_06-19-2023T20.33.47/k_marginal_synopsys/subsample_error_comparison.csv @@ -0,0 +1,12 @@ +,Sub-Sample Size,Sub-Sample K-Marginal Score,Deidentified Data K-marginal score,Absolute Diff. 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diff --git a/sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_05-19-2023T18.01.12/pearson_correlation/correlation_difference.csv b/sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_national2019_06-19-2023T20.33.47/pearson_correlation/correlation_difference.csv similarity index 100% rename from sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_05-19-2023T18.01.12/pearson_correlation/correlation_difference.csv rename to sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_national2019_06-19-2023T20.33.47/pearson_correlation/correlation_difference.csv diff --git a/sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_05-19-2023T18.01.12/pearson_correlation/pearson_corr_diff.jpg b/sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_national2019_06-19-2023T20.33.47/pearson_correlation/pearson_corr_diff.jpg similarity index 100% rename from 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b/sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_national2019_06-19-2023T20.33.47/propensity/propensity_distribution.jpg new file mode 100644 index 0000000..a374687 Binary files /dev/null and b/sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_national2019_06-19-2023T20.33.47/propensity/propensity_distribution.jpg differ diff --git a/sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_05-19-2023T18.01.12/report.html b/sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_national2019_06-19-2023T20.33.47/report.html similarity index 95% rename from sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_05-19-2023T18.01.12/report.html rename to sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_national2019_06-19-2023T20.33.47/report.html index 7138e5d..f7f0404 100644 --- a/sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_05-19-2023T18.01.12/report.html +++ b/sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_national2019_06-19-2023T20.33.47/report.html @@ -160,11 +160,11 @@

Data Evaluation Report


-

Report created on: May 19, 2023 18:19:53

+

Report created on: June 19, 2023 20:54:12

Created with - SDNIST v2.2.1 + SDNIST v2.3.0

@@ -230,7 +230,67 @@

- Library + Target Dataset + + + + national2019 + + + + + + + + + + + Epsilon + + + + 10 + + + + + + + + + + + Variant Label + + + + + + + + + + + + + + + Algorithm Type + + + + histogram + + + + + + + + + + + Library Name @@ -245,7 +305,7 @@

- Feature Set + Feature Set Name @@ -260,11 +320,11 @@

- Target Dataset + Privacy Category - national2019 + dp @@ -275,11 +335,11 @@

- Epsilon + Deid Data Id - 10 + 08bbf24efc4d94c0a6291fdff1f94e4430ee668e @@ -290,11 +350,11 @@

- Variant Label + Features List - + SEX, MSP, RAC1P, HOUSING_TYPE, OWN_RENT, EDU, PINCP_DECILE, DVET @@ -305,11 +365,56 @@

- Privacy + Privacy Label Detail - Differential Privacy + Differentially private, histogram-based synthetic data. The DP geometric mechanism is used to produce a noisy integer occurrence count for each possible record value. + + + + + + + + + + + Submission Timestamp + + + + 5/20/2023 00:00:00 + + + + + + + + + + + Team + + + + CRC + + + + + + + + + + + Research Papers + + + + https://web.cs.ucdavis.edu/~franklin/ecs289/2010/dwork_2008.pdf @@ -907,11 +1012,11 @@

- 10% + 1% - 971 + 905 @@ -919,7 +1024,7 @@

- 27 + 93 @@ -930,11 +1035,11 @@

- 20% + 5% - 978 + 963 @@ -942,7 +1047,7 @@

- 20 + 35 @@ -953,11 +1058,11 @@

- 30% + 10% - 985 + 971 @@ -965,7 +1070,7 @@

- 13 + 27 @@ -976,11 +1081,11 @@

- 40% + 20% - 987 + 983 @@ -988,7 +1093,7 @@

- 11 + 15 @@ -999,11 +1104,11 @@

- 50% + 30% - 990 + 982 @@ -1011,7 +1116,7 @@

- 8 + 16 @@ -1022,11 +1127,11 @@

- 60% + 40% - 992 + 989 @@ -1034,7 +1139,7 @@

- 6 + 9 @@ -1045,11 +1150,11 @@

- 70% + 50% - 993 + 990 @@ -1057,7 +1162,7 @@

- 5 + 8 @@ -1068,11 +1173,11 @@

- 80% + 60% - 995 + 992 @@ -1080,7 +1185,7 @@

- 3 + 6 @@ -1088,14 +1193,14 @@

- + - 90% + 70% - 996 + 994 @@ -1103,10 +1208,30 @@

- 2 + 4 + + + + + + + + + + + 80% + + + + 995 + + + + 998 + 3 @@ -1114,14 +1239,14 @@

- + - 100% + 90% - 1000 + 997 @@ -1129,7 +1254,10 @@

- 2 + 1 + + + @@ -3193,7 +3321,7 @@

- This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you’re using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes– the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive. + This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you're using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.
@@ -3476,7 +3604,7 @@

- The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP = “N”, individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. + The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP='N', individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape.
@@ -3586,6 +3714,34 @@

+
+ +
+ + + + +
+ In real world tabular data, it's common for record features to have some deterministic, publicly known dependencies on each other: knowing someone's AGEP= 3 necessarily tells you something about their marital status, income and educational attainment. Different deidentification methods may be better or worse at automatically preserving these relationships. When they fail (ex: producing toddlers with PhDs) we say those records contain "inconsistencies". Our consistency check metric below is not exhaustive, we don't catch everything the way a production-grade system should, but this will give you a sense of how consistency is preserved for age, work and household features. Note that different deidentification approaches may do better or worse with different types of inconsistencies. +
+ + + +
+ + + + + + + + + + + + + +

@@ -5029,6 +5185,7 @@

Privacy Evaluation

+

@@ -5056,7 +5213,91 @@

- This is a count of unique records in the target data that were exactly reproduced in the deidentified data. Because these records were unique outliers in the target data, and they still appear unchanged in the deidentified data, they are potentially vulnerable to reidentification. + Unique Exact Match (UEM) is a simple privacy metric that counts the percentage of singleton records in the target that are also present in the deidentified data; these uniquely identifiable individuals leaked through the deidentification process. +
+ + + +

+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ Below we also include an estimate of the feature space size. The feature space is the set of all possible record values given the selected target data and feature subset. For instance, if we had two features, Hat [cap, bonnet] and Color [green, blue, purple], our feature space would consist of 2 x 3 = 6 possible combinations (e.g. 'green cap', 'blue bonnet'). Note that feature spaces are based on the feature set, not on what records actually exist in the data. Purple bonnet is a possible combination in this feature space, but it's likely no one in the hypothetical data owns a purple bonnet (and the count of that record value would be 0). +
+ + + +
+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ As we add features to the feature set, we increase the size of the feature space, but we don't change the actual number of records in the data-- it's the same people, but now they're spread out more thinly across a wider set of possible record values. Large feature spaces will disperse populations very sparsely (most possible record values will have count 0 or 1) and as a result the data will contain very many uniquely identifiable records. Intuitively, once you know enough pieces of information about someone, everyone becomes very distinct from everyone else. This can pose a challenge for privacy. +
+ + + +
+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ The Target Data Properties below provides an estimate of the feature space size (100 is used for continuous feature), along with the portion of the records in the ground truth target data that are unique (ie, they are the only person with that record value, they have a count of 1). The Deidentified Data Properties reports the percentage of those uniquely identifiable individuals that are still present in the deidentified data. Because they are unique, real records, they are potentially vulnerable to reidentification.
@@ -5100,7 +5341,7 @@

- 1,135,134 + 1.135e+06 @@ -5162,7 +5403,7 @@

- 2773 (10.18%) + 2773 (100.0%) @@ -5189,6 +5430,12 @@

+
+
+
+
+ +

@@ -5279,7 +5526,7 @@

- OWN_RENT, RAC1P, SEX, EDU, MSP + MSP, RAC1P, SEX, EDU, OWN_RENT
@@ -8153,6 +8400,34 @@

+
+ +
+ + + + +
+ There are a total of 271 possible codes for INDP, 269 of these codes appear in the Diverse Community Data Excerpts (233 in MA, 264 in Texas and National) +
+ + + +
+ + + + + + + + + + + + + +

diff --git a/sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_05-19-2023T18.01.12/ui.json b/sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_national2019_06-19-2023T20.33.47/ui.json similarity index 93% rename from sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_05-19-2023T18.01.12/ui.json rename to sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_national2019_06-19-2023T20.33.47/ui.json index fa8fb95..3081524 100644 --- a/sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_05-19-2023T18.01.12/ui.json +++ b/sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_national2019_06-19-2023T20.33.47/ui.json @@ -1,6 +1,6 @@ { - "Created on": "May 19, 2023 18:19:53", - "version": "2.2.1", + "Created on": "June 19, 2023 20:54:12", + "version": "2.3.0", "data_description": { "target": [ { @@ -42,14 +42,6 @@ "Label Name": "Algorithm Name", "Label Value": "DPHist" }, - { - "Label Name": "Library", - "Label Value": "tumult" - }, - { - "Label Name": "Feature Set", - "Label Value": "demographic-focused-except-AGEP-DEYE" - }, { "Label Name": "Target Dataset", "Label Value": "national2019" @@ -63,8 +55,44 @@ "Label Value": "" }, { - "Label Name": "Privacy", - "Label Value": "Differential Privacy" + "Label Name": "Algorithm Type", + "Label Value": "histogram" + }, + { + "Label Name": "Library Name", + "Label Value": "tumult" + }, + { + "Label Name": "Feature Set Name", + "Label Value": "demographic-focused-except-AGEP-DEYE" + }, + { + "Label Name": "Privacy Category", + "Label Value": "dp" + }, + { + "Label Name": "Deid Data Id", + "Label Value": "08bbf24efc4d94c0a6291fdff1f94e4430ee668e" + }, + { + "Label Name": "Features List", + "Label Value": "SEX, MSP, RAC1P, HOUSING_TYPE, OWN_RENT, EDU, PINCP_DECILE, DVET" + }, + { + "Label Name": "Privacy Label Detail", + "Label Value": "Differentially private, histogram-based synthetic data. The DP geometric mechanism is used to produce a noisy integer occurrence count for each possible record value. " + }, + { + "Label Name": "Submission Timestamp", + "Label Value": "5/20/2023 00:00:00" + }, + { + "Label Name": "Team", + "Label Value": "CRC" + }, + { + "Label Name": "Research Papers", + "Label Value": "https://web.cs.ucdavis.edu/~franklin/ecs289/2010/dwork_2008.pdf" } ], "validations": [] @@ -153,6 +181,18 @@ { "name": null, "data": [ + { + "Sub-Sample Size": "1%", + "Sub-Sample K-Marginal Score": 905, + "Deidentified Data K-marginal score": 998, + "Absolute Diff. From Deidentified Data K-marginal Score": "93" + }, + { + "Sub-Sample Size": "5%", + "Sub-Sample K-Marginal Score": 963, + "Deidentified Data K-marginal score": 998, + "Absolute Diff. From Deidentified Data K-marginal Score": "35" + }, { "Sub-Sample Size": "10%", "Sub-Sample K-Marginal Score": 971, @@ -161,21 +201,21 @@ }, { "Sub-Sample Size": "20%", - "Sub-Sample K-Marginal Score": 978, + "Sub-Sample K-Marginal Score": 983, "Deidentified Data K-marginal score": 998, - "Absolute Diff. From Deidentified Data K-marginal Score": "20" + "Absolute Diff. From Deidentified Data K-marginal Score": "15" }, { "Sub-Sample Size": "30%", - "Sub-Sample K-Marginal Score": 985, + "Sub-Sample K-Marginal Score": 982, "Deidentified Data K-marginal score": 998, - "Absolute Diff. From Deidentified Data K-marginal Score": "13" + "Absolute Diff. From Deidentified Data K-marginal Score": "16" }, { "Sub-Sample Size": "40%", - "Sub-Sample K-Marginal Score": 987, + "Sub-Sample K-Marginal Score": 989, "Deidentified Data K-marginal score": 998, - "Absolute Diff. From Deidentified Data K-marginal Score": "11" + "Absolute Diff. From Deidentified Data K-marginal Score": "9" }, { "Sub-Sample Size": "50%", @@ -191,9 +231,9 @@ }, { "Sub-Sample Size": "70%", - "Sub-Sample K-Marginal Score": 993, + "Sub-Sample K-Marginal Score": 994, "Deidentified Data K-marginal score": 998, - "Absolute Diff. From Deidentified Data K-marginal Score": "5" + "Absolute Diff. From Deidentified Data K-marginal Score": "4" }, { "Sub-Sample Size": "80%", @@ -203,16 +243,10 @@ }, { "Sub-Sample Size": "90%", - "Sub-Sample K-Marginal Score": 996, + "Sub-Sample K-Marginal Score": 997, "Deidentified Data K-marginal score": 998, - "Absolute Diff. From Deidentified Data K-marginal Score": "2", + "Absolute Diff. From Deidentified Data K-marginal Score": "1", "min_idx": true - }, - { - "Sub-Sample Size": "100%", - "Sub-Sample K-Marginal Score": 1000, - "Deidentified Data K-marginal score": 998, - "Absolute Diff. From Deidentified Data K-marginal Score": "2" } ], "type": "table", @@ -847,7 +881,7 @@ "-1": [ { "name": null, - "data": "This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you\u2019re using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes\u2013 the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.", + "data": "This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you're using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.", "type": "string", "dotted_break": false }, @@ -914,7 +948,7 @@ }, { "name": null, - "data": "The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP = \u201cN\u201d, individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. ", + "data": "The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP='N', individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. ", "type": "string", "dotted_break": false }, @@ -946,6 +980,12 @@ "metric_name": "Inconsistencies", "attachments": { "-1": [ + { + "name": null, + "data": "In real world tabular data, it's common for record features to have some deterministic, publicly known dependencies on each other: knowing someone's AGEP= 3 necessarily tells you something about their marital status, income and educational attainment. Different deidentification methods may be better or worse at automatically preserving these relationships. When they fail (ex: producing toddlers with PhDs) we say those records contain \"inconsistencies\". Our consistency check metric below is not exhaustive, we don't catch everything the way a production-grade system should, but this will give you a sense of how consistency is preserved for age, work and household features. Note that different deidentification approaches may do better or worse with different types of inconsistencies.", + "type": "string", + "dotted_break": false + }, { "name": "Summary", "data": { @@ -1177,19 +1217,37 @@ "-1": [ { "name": null, - "data": "This is a count of unique records in the target data that were exactly reproduced in the deidentified data. Because these records were unique outliers in the target data, and they still appear unchanged in the deidentified data, they are potentially vulnerable to reidentification.", + "data": "Unique Exact Match (UEM) is a simple privacy metric that counts the percentage of singleton records in the target that are also present in the deidentified data; these uniquely identifiable individuals leaked through the deidentification process.", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "Below we also include an estimate of the feature space size. The feature space is the set of all possible record values given the selected target data and feature subset. For instance, if we had two features, Hat [cap, bonnet] and Color [green, blue, purple], our feature space would consist of 2 x 3 = 6 possible combinations (e.g. 'green cap', 'blue bonnet'). Note that feature spaces are based on the feature set, not on what records actually exist in the data. Purple bonnet is a possible combination in this feature space, but it's likely no one in the hypothetical data owns a purple bonnet (and the count of that record value would be 0).", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "As we add features to the feature set, we increase the size of the feature space, but we don't change the actual number of records in the data-- it's the same people, but now they're spread out more thinly across a wider set of possible record values. Large feature spaces will disperse populations very sparsely (most possible record values will have count 0 or 1) and as a result the data will contain very many uniquely identifiable records. Intuitively, once you know enough pieces of information about someone, everyone becomes very distinct from everyone else. This can pose a challenge for privacy. ", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "The Target Data Properties below provides an estimate of the feature space size (100 is used for continuous feature), along with the portion of the records in the ground truth target data that are unique (ie, they are the only person with that record value, they have a count of 1). The Deidentified Data Properties reports the percentage of those uniquely identifiable individuals that are still present in the deidentified data. Because they are unique, real records, they are potentially vulnerable to reidentification.", "type": "string", "dotted_break": false }, { "name": "Target Data Properties", - "data": "Feature space size (possible combinations): -Highlight-1,135,134-Highlight-
Number of unique records in Target Data: -Highlight-2773 (10.18%-Highlight-)", + "data": "Feature space size (possible combinations): -Highlight-1.135e+06-Highlight-
Number of unique records in Target Data: -Highlight-2773 (10.18%-Highlight-)", "type": "string", "dotted_break": false }, { "name": "Deidentified Data Properties", - "data": "Number of unique Target Data records exactly matched in Deid. Data: -Highlight-2773 (10.18%)-Highlight-", + "data": "Number of unique Target Data records exactly matched in Deid. Data: -Highlight-2773 (100.0%)-Highlight-", "type": "string", "dotted_break": false } @@ -1214,7 +1272,7 @@ }, { "name": null, - "data": "OWN_RENT, RAC1P, SEX, EDU, MSP", + "data": "MSP, RAC1P, SEX, EDU, OWN_RENT", "type": "string", "dotted_break": false }, @@ -1830,6 +1888,12 @@ "type": "string", "dotted_break": false }, + { + "name": null, + "data": "There are a total of 271 possible codes for INDP, 269 of these codes appear in the Diverse Community Data Excerpts (233 in MA, 264 in Texas and National)", + "type": "string", + "dotted_break": false + }, { "name": "INDP_CAT: Industry categories", "data": [ diff --git a/sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_05-19-2023T18.01.12/univariate/DVET.jpg b/sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_national2019_06-19-2023T20.33.47/univariate/DVET.jpg similarity index 100% rename from sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_05-19-2023T18.01.12/univariate/DVET.jpg rename to 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b/sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_national2019_06-19-2023T20.33.47/univariate/divergence.csv similarity index 100% rename from sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_05-19-2023T18.01.12/univariate/divergence.csv rename to sdnist/report/sample-reports/report_dphist_e_10_cf8_na2019_national2019_06-19-2023T20.33.47/univariate/divergence.csv diff --git a/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/k_marginal_synopsys/subsample_error_comparison.csv b/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/k_marginal_synopsys/subsample_error_comparison.csv deleted file mode 100644 index 7d313ab..0000000 --- a/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/k_marginal_synopsys/subsample_error_comparison.csv +++ /dev/null @@ -1,11 +0,0 @@ -,Sub-Sample Size,Sub-Sample K-Marginal Score,Deidentified Data K-marginal score,Absolute Diff. From Deidentified Data K-marginal Score -0,10%,837,921,84 -1,20%,889,921,32 -2,30%,919,921,2 -3,40%,932,921,11 -4,50%,946,921,25 -5,60%,955,921,34 -6,70%,964,921,43 -7,80%,972,921,51 -8,90%,982,921,61 -9,100%,1000,921,79 diff --git a/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/pca/components_eigenvector.csv b/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/pca/components_eigenvector.csv deleted file mode 100644 index d6b2974..0000000 --- a/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/pca/components_eigenvector.csv +++ /dev/null @@ -1,6 +0,0 @@ -,AGEP,DEAR,DENSITY,DEYE,DPHY,DREM,DVET,EDU,HISP,HOUSING_TYPE,INDP,INDP_CAT,MSP,NOC,NPF,OWN_RENT,PINCP,PINCP_DECILE,POVPIP,PUMA,PWGTP,RAC1P,SEX,WGTP 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diff --git a/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/k_marginal_breakdown/univariate/indp_indp_cat_4.jpg b/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_national2019_06-19-2023T20.33.47/k_marginal_breakdown/univariate/indp_indp_cat_4.jpg similarity index 100% rename from sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/k_marginal_breakdown/univariate/indp_indp_cat_4.jpg rename to sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_national2019_06-19-2023T20.33.47/k_marginal_breakdown/univariate/indp_indp_cat_4.jpg diff --git a/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/k_marginal_breakdown/univariate/indp_indp_cat_5.jpg b/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_national2019_06-19-2023T20.33.47/k_marginal_breakdown/univariate/indp_indp_cat_5.jpg similarity 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index 8135543..f97cda5 100644 --- a/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv +++ b/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_national2019_06-19-2023T20.33.47/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv @@ -1,6 +1,6 @@ ,PUMA,40% Target Subsample Baseline,Deidentified Data Score -0,01-01301,815,785 -1,17-03531,829,806 -2,29-01901,836,809 +0,01-01301,816,785 +1,17-03531,841,806 +2,29-01901,835,809 3,36-03710,837,810 -4,13-04600,858,817 +4,13-04600,859,817 diff --git a/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv b/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv similarity index 82% rename from sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv rename to sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv index f694bb7..665e23c 100644 --- a/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv +++ b/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv @@ -1,21 +1,21 @@ ,PUMA,40% Target Subsample Baseline,Deidentified Data Score -0,"['01-01301', 'Birmingham City (West)', 'https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/']",815,785 -1,"['17-03531', 'Chicago City (South)--Auburn Gresham, Roseland, Chatham, Avalon Park & Burnside', 'https://censusreporter.org/profiles/79500US1703531-chicago-city-south-auburn-gresham-roseland-chatham-avalon-park-burnside-puma-il/']",829,806 -2,"['29-01901', 'St. Louis City (North)', 'https://censusreporter.org/profiles/79500US2901901-st-louis-city-north-puma-mo/']",836,809 +0,"['01-01301', 'Birmingham City (West)', 'https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/']",816,785 +1,"['17-03531', 'Chicago City (South)--Auburn Gresham, Roseland, Chatham, Avalon Park & Burnside', 'https://censusreporter.org/profiles/79500US1703531-chicago-city-south-auburn-gresham-roseland-chatham-avalon-park-burnside-puma-il/']",841,806 +2,"['29-01901', 'St. Louis City (North)', 'https://censusreporter.org/profiles/79500US2901901-st-louis-city-north-puma-mo/']",835,809 3,"['36-03710', 'NYC-Bronx Community District 1 & 2--Hunts Point, Longwood & Melrose', 'https://censusreporter.org/profiles/79500US3603710-nyc-bronx-community-district-1-2-hunts-point-longwood-melrose-puma-ny/']",837,810 -4,"['13-04600', 'Atlanta Regional Commission--Fulton County (Central)--Atlanta City (Central)', 'https://censusreporter.org/profiles/79500US1304600-atlanta-regional-commission-fulton-county-central-atlanta-city-central-puma-ga/']",858,817 +4,"['13-04600', 'Atlanta Regional Commission--Fulton County (Central)--Atlanta City (Central)', 'https://censusreporter.org/profiles/79500US1304600-atlanta-regional-commission-fulton-county-central-atlanta-city-central-puma-ga/']",859,817 5,"['36-04010', 'NYC-Brooklyn Community District 17--East Flatbush, Farragut & Rugby', 'https://censusreporter.org/profiles/79500US3604010-nyc-brooklyn-community-district-17-east-flatbush-farragut-rugby-puma-ny/']",861,818 -6,"['28-01100', 'Central Region--Jackson City (East & Central)', 'https://censusreporter.org/profiles/79500US2801100-central-region-jackson-city-east-central-puma-ms/']",850,823 -7,"['26-02702', 'Washtenaw County (East Central)--Ann Arbor City Area', 'https://censusreporter.org/profiles/79500US2602702-washtenaw-county-east-central-ann-arbor-city-area-puma-mi/']",846,823 -8,"['32-00405', 'Las Vegas City (Southeast)', 'https://censusreporter.org/profiles/79500US3200405-las-vegas-city-southeast-puma-nv/']",849,824 -9,"['40-00200', 'Cherokee, Sequoyah & Adair Counties', 'https://censusreporter.org/profiles/79500US4000200-cherokee-sequoyah-adair-counties-puma-ok/']",854,828 -10,"['19-01700', 'Des Moines City', 'https://censusreporter.org/profiles/79500US1901700-des-moines-city-puma-ia/']",859,829 -11,"['06-07502', 'San Francisco County (North & East)--North Beach & Chinatown', 'https://censusreporter.org/profiles/79500US0607502-san-francisco-county-north-east-north-beach-chinatown-puma-ca/']",842,835 -12,"['08-00803', 'Boulder County (Central)--Boulder City', 'https://censusreporter.org/profiles/79500US0800803-boulder-county-central-boulder-city-puma-co/']",865,845 -13,"['51-01301', 'Arlington County (North)', 'https://censusreporter.org/profiles/79500US5101301-arlington-county-north-puma-va/']",867,850 -14,"['17-03529', 'Chicago City (South)--South Shore, Hyde Park, Woodlawn, Grand Boulevard & Douglas', 'https://censusreporter.org/profiles/79500US1703529-chicago-city-south-south-shore-hyde-park-woodlawn-grand-boulevard-douglas-puma-il/']",871,852 -15,"['30-00600', 'East Montana (Outside Billings City)', 'https://censusreporter.org/profiles/79500US3000600-east-montana-outside-billings-city-puma-mt/']",885,862 -16,"['38-00100', 'West North Dakota--Minot City', 'https://censusreporter.org/profiles/79500US3800100-west-north-dakota-minot-city-puma-nd/']",890,864 -17,"['06-08507', 'Santa Clara County (Southwest)--Cupertino, Saratoga Cities & Los Gatos Town', 'https://censusreporter.org/profiles/79500US0608507-santa-clara-county-southwest-cupertino-saratoga-cities-los-gatos-town-puma-ca/']",885,865 -18,"['51-51255', 'Alexandria City', 'https://censusreporter.org/profiles/79500US5151255-alexandria-city-puma-va/']",875,869 -19,"['24-01004', 'Montgomery County (South)--Bethesda, Potomac & North Bethesda', 'https://censusreporter.org/profiles/79500US2401004-montgomery-county-south-bethesda-potomac-north-bethesda-puma-md/']",907,883 +6,"['28-01100', 'Central Region--Jackson City (East & Central)', 'https://censusreporter.org/profiles/79500US2801100-central-region-jackson-city-east-central-puma-ms/']",853,823 +7,"['26-02702', 'Washtenaw County (East Central)--Ann Arbor City Area', 'https://censusreporter.org/profiles/79500US2602702-washtenaw-county-east-central-ann-arbor-city-area-puma-mi/']",849,823 +8,"['32-00405', 'Las Vegas City (Southeast)', 'https://censusreporter.org/profiles/79500US3200405-las-vegas-city-southeast-puma-nv/']",857,824 +9,"['40-00200', 'Cherokee, Sequoyah & Adair Counties', 'https://censusreporter.org/profiles/79500US4000200-cherokee-sequoyah-adair-counties-puma-ok/']",858,828 +10,"['19-01700', 'Des Moines City', 'https://censusreporter.org/profiles/79500US1901700-des-moines-city-puma-ia/']",860,829 +11,"['06-07502', 'San Francisco County (North & East)--North Beach & Chinatown', 'https://censusreporter.org/profiles/79500US0607502-san-francisco-county-north-east-north-beach-chinatown-puma-ca/']",850,835 +12,"['08-00803', 'Boulder County (Central)--Boulder City', 'https://censusreporter.org/profiles/79500US0800803-boulder-county-central-boulder-city-puma-co/']",873,845 +13,"['51-01301', 'Arlington County (North)', 'https://censusreporter.org/profiles/79500US5101301-arlington-county-north-puma-va/']",870,850 +14,"['17-03529', 'Chicago City (South)--South Shore, Hyde Park, Woodlawn, Grand Boulevard & Douglas', 'https://censusreporter.org/profiles/79500US1703529-chicago-city-south-south-shore-hyde-park-woodlawn-grand-boulevard-douglas-puma-il/']",866,852 +15,"['30-00600', 'East Montana (Outside Billings City)', 'https://censusreporter.org/profiles/79500US3000600-east-montana-outside-billings-city-puma-mt/']",884,862 +16,"['38-00100', 'West North Dakota--Minot City', 'https://censusreporter.org/profiles/79500US3800100-west-north-dakota-minot-city-puma-nd/']",891,864 +17,"['06-08507', 'Santa Clara County (Southwest)--Cupertino, Saratoga Cities & Los Gatos Town', 'https://censusreporter.org/profiles/79500US0608507-santa-clara-county-southwest-cupertino-saratoga-cities-los-gatos-town-puma-ca/']",883,865 +18,"['51-51255', 'Alexandria City', 'https://censusreporter.org/profiles/79500US5151255-alexandria-city-puma-va/']",876,869 +19,"['24-01004', 'Montgomery County (South)--Bethesda, Potomac & North Bethesda', 'https://censusreporter.org/profiles/79500US2401004-montgomery-county-south-bethesda-potomac-north-bethesda-puma-md/']",906,883 diff --git a/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_national2019_06-19-2023T20.33.47/k_marginal_synopsys/subsample_error_comparison.csv b/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_national2019_06-19-2023T20.33.47/k_marginal_synopsys/subsample_error_comparison.csv new file mode 100644 index 0000000..a10142a --- /dev/null +++ b/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_national2019_06-19-2023T20.33.47/k_marginal_synopsys/subsample_error_comparison.csv @@ -0,0 +1,12 @@ +,Sub-Sample Size,Sub-Sample K-Marginal Score,Deidentified Data K-marginal score,Absolute Diff. 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sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/report.html rename to sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_national2019_06-19-2023T20.33.47/report.html index c8d4d1e..5845c1a 100644 --- a/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/report.html +++ b/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_national2019_06-19-2023T20.33.47/report.html @@ -160,11 +160,11 @@

Data Evaluation Report


-

Report created on: May 19, 2023 18:11:36

+

Report created on: June 19, 2023 20:44:31

Created with - SDNIST v2.2.1 + SDNIST v2.3.0

@@ -275,11 +275,11 @@

- Feature Set + Variant Label - all-features + national2019 @@ -290,11 +290,11 @@

- Variant Label + Submission Number - national2019 + 2 @@ -305,11 +305,11 @@

- Submission Number + Algorithm Type - 2 + neural net @@ -320,11 +320,11 @@

- Privacy + Feature Set Name - Synthetic Data (Non-differentially Private) + all-features @@ -335,7 +335,22 @@

- Library + Privacy Category + + + + non_dp + + + + + + + + + + + Library Name @@ -344,6 +359,66 @@

+ + + + + + + Deid Data Id + + + + 583b01386728a111f87fd1ef4d8df8c485eb5789 + + + + + + + + + + + Features List + + + + PUMA, AGEP, SEX, MSP, HISP, RAC1P, NOC, NPF, HOUSING_TYPE, OWN_RENT, DENSITY, INDP, INDP_CAT, EDU, PINCP, PINCP_DECILE, POVPIP, DVET, DREM, DPHY, DEYE, DEAR, WGTP, PWGTP + + + + + + + + + + + Privacy Label Detail + + + + The MOSTLY AI Synthetic Data Platform generates synthetic data based original input data. The usage of synthetic data minimizes privacy risks - such as membership inference, re-identification, and attribute inference - while maintaining the majority of statistical properties of the original data. The privacy of the original data is protected by, first, applying basic outlier protection mechanisms and, second, strong regularization of the generative networks creating the synthetic data. + + + + + + + + + + + Research Papers + + + + https://doi.org/10.48550/arXiv.2104.03007 + + + +

@@ -1305,11 +1380,11 @@

- 10% + 1% - 837 + 567 @@ -1317,7 +1392,7 @@

- 84 + 354 @@ -1328,11 +1403,11 @@

- 20% + 5% - 889 + 774 @@ -1340,7 +1415,7 @@

- 32 + 147 @@ -1348,14 +1423,14 @@

- + - 30% + 10% - 919 + 840 @@ -1363,10 +1438,30 @@

- 2 + 81 + + + + + + + + + + + 20% + + + + 886 + + + + 921 + 35 @@ -1374,14 +1469,14 @@

- + - 40% + 30% - 932 + 917 @@ -1389,7 +1484,10 @@

- 11 + 4 + + + @@ -1400,11 +1498,11 @@

- 50% + 40% - 946 + 933 @@ -1412,7 +1510,7 @@

- 25 + 12 @@ -1423,11 +1521,11 @@

- 60% + 50% - 955 + 944 @@ -1435,7 +1533,7 @@

- 34 + 23 @@ -1446,11 +1544,11 @@

- 70% + 60% - 964 + 955 @@ -1458,7 +1556,7 @@

- 43 + 34 @@ -1469,11 +1567,11 @@

- 80% + 70% - 972 + 964 @@ -1481,7 +1579,7 @@

- 51 + 43 @@ -1492,11 +1590,11 @@

- 90% + 80% - 982 + 973 @@ -1504,7 +1602,7 @@

- 61 + 52 @@ -1515,11 +1613,11 @@

- 100% + 90% - 1000 + 981 @@ -1527,7 +1625,7 @@

- 79 + 60 @@ -1631,7 +1729,7 @@

- 815 + 816 @@ -1653,7 +1751,7 @@

- 829 + 841 @@ -1675,7 +1773,7 @@

- 836 + 835 @@ -1719,7 +1817,7 @@

- 858 + 859 @@ -1763,7 +1861,7 @@

- 850 + 853 @@ -1785,7 +1883,7 @@

- 846 + 849 @@ -1807,7 +1905,7 @@

- 849 + 857 @@ -1829,7 +1927,7 @@

- 854 + 858 @@ -1851,7 +1949,7 @@

- 859 + 860 @@ -1873,7 +1971,7 @@

- 842 + 850 @@ -1895,7 +1993,7 @@

- 865 + 873 @@ -1917,7 +2015,7 @@

- 867 + 870 @@ -1939,7 +2037,7 @@

- 871 + 866 @@ -1961,7 +2059,7 @@

- 885 + 884 @@ -1983,7 +2081,7 @@

- 890 + 891 @@ -2005,7 +2103,7 @@

- 885 + 883 @@ -2027,7 +2125,7 @@

- 875 + 876 @@ -2049,7 +2147,7 @@

- 907 + 906 @@ -5269,7 +5367,7 @@

- This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you’re using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes– the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive. + This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you're using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.
@@ -5552,7 +5650,7 @@

- The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP = “N”, individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. + The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP='N', individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape.
@@ -5662,6 +5760,34 @@

+
+ +
+ + + + +
+ In real world tabular data, it's common for record features to have some deterministic, publicly known dependencies on each other: knowing someone's AGEP= 3 necessarily tells you something about their marital status, income and educational attainment. Different deidentification methods may be better or worse at automatically preserving these relationships. When they fail (ex: producing toddlers with PhDs) we say those records contain "inconsistencies". Our consistency check metric below is not exhaustive, we don't catch everything the way a production-grade system should, but this will give you a sense of how consistency is preserved for age, work and household features. Note that different deidentification approaches may do better or worse with different types of inconsistencies. +
+ + + +
+ + + + + + + + + + + + + +

@@ -8759,7 +8885,7 @@

- K-Marginal Score Breakdown: + Worst Performing PUMAs Breakdown:

@@ -8783,7 +8909,7 @@

- In the metrics above we’ve considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score. + In the metrics above we've considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score.
@@ -8882,7 +9008,7 @@

- 815 + 816 @@ -8904,7 +9030,7 @@

- 829 + 841 @@ -8926,7 +9052,7 @@

- 836 + 835 @@ -8970,7 +9096,7 @@

- 858 + 859 @@ -9422,6 +9548,7 @@

Privacy Evaluation

+

@@ -9449,7 +9576,91 @@

- This is a count of unique records in the target data that were exactly reproduced in the deidentified data. Because these records were unique outliers in the target data, and they still appear unchanged in the deidentified data, they are potentially vulnerable to reidentification. + Unique Exact Match (UEM) is a simple privacy metric that counts the percentage of singleton records in the target that are also present in the deidentified data; these uniquely identifiable individuals leaked through the deidentification process. +
+ + + +

+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ Below we also include an estimate of the feature space size. The feature space is the set of all possible record values given the selected target data and feature subset. For instance, if we had two features, Hat [cap, bonnet] and Color [green, blue, purple], our feature space would consist of 2 x 3 = 6 possible combinations (e.g. 'green cap', 'blue bonnet'). Note that feature spaces are based on the feature set, not on what records actually exist in the data. Purple bonnet is a possible combination in this feature space, but it's likely no one in the hypothetical data owns a purple bonnet (and the count of that record value would be 0). +
+ + + +
+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ As we add features to the feature set, we increase the size of the feature space, but we don't change the actual number of records in the data-- it's the same people, but now they're spread out more thinly across a wider set of possible record values. Large feature spaces will disperse populations very sparsely (most possible record values will have count 0 or 1) and as a result the data will contain very many uniquely identifiable records. Intuitively, once you know enough pieces of information about someone, everyone becomes very distinct from everyone else. This can pose a challenge for privacy. +
+ + + +
+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ The Target Data Properties below provides an estimate of the feature space size (100 is used for continuous feature), along with the portion of the records in the ground truth target data that are unique (ie, they are the only person with that record value, they have a count of 1). The Deidentified Data Properties reports the percentage of those uniquely identifiable individuals that are still present in the deidentified data. Because they are unique, real records, they are potentially vulnerable to reidentification.
@@ -9493,7 +9704,7 @@

- 3,407,717,673,360,000 + 5.178e+26 @@ -9582,6 +9793,12 @@

+
+
+
+
+ +

@@ -9672,7 +9889,7 @@

- HISP, PUMA, OWN_RENT, EDU, MSP, SEX, RAC1P, INDP_CAT + MSP, PUMA, INDP_CAT, EDU, SEX, HISP, OWN_RENT, RAC1P
@@ -12546,6 +12763,34 @@

+
+ +
+ + + + +
+ There are a total of 271 possible codes for INDP, 269 of these codes appear in the Diverse Community Data Excerpts (233 in MA, 264 in Texas and National) +
+ + + +
+ + + + + + + + + + + + + +

diff --git a/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/ui.json b/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_national2019_06-19-2023T20.33.47/ui.json similarity index 94% rename from sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/ui.json rename to sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_national2019_06-19-2023T20.33.47/ui.json index a74c3e7..d64e220 100644 --- a/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/ui.json +++ b/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_national2019_06-19-2023T20.33.47/ui.json @@ -1,6 +1,6 @@ { - "Created on": "May 19, 2023 18:11:36", - "version": "2.2.1", + "Created on": "June 19, 2023 20:44:31", + "version": "2.3.0", "data_description": { "target": [ { @@ -54,10 +54,6 @@ "Label Name": "Algorithm Name", "Label Value": "MostlyAI SD" }, - { - "Label Name": "Feature Set", - "Label Value": "all-features" - }, { "Label Name": "Variant Label", "Label Value": "national2019" @@ -67,12 +63,36 @@ "Label Value": 2 }, { - "Label Name": "Privacy", - "Label Value": "Synthetic Data (Non-differentially Private)" + "Label Name": "Algorithm Type", + "Label Value": "neural net" + }, + { + "Label Name": "Feature Set Name", + "Label Value": "all-features" + }, + { + "Label Name": "Privacy Category", + "Label Value": "non_dp" }, { - "Label Name": "Library", + "Label Name": "Library Name", "Label Value": "MostlyAI SD" + }, + { + "Label Name": "Deid Data Id", + "Label Value": "583b01386728a111f87fd1ef4d8df8c485eb5789" + }, + { + "Label Name": "Features List", + "Label Value": "PUMA, AGEP, SEX, MSP, HISP, RAC1P, NOC, NPF, HOUSING_TYPE, OWN_RENT, DENSITY, INDP, INDP_CAT, EDU, PINCP, PINCP_DECILE, POVPIP, DVET, DREM, DPHY, DEYE, DEAR, WGTP, PWGTP" + }, + { + "Label Name": "Privacy Label Detail", + "Label Value": "The MOSTLY AI Synthetic Data Platform generates synthetic data based original input data. The usage of synthetic data minimizes privacy risks - such as membership inference, re-identification, and attribute inference - while maintaining the majority of statistical properties of the original data. The privacy of the original data is protected by, first, applying basic outlier protection mechanisms and, second, strong regularization of the generative networks creating the synthetic data." + }, + { + "Label Name": "Research Papers", + "Label Value": "https://doi.org/10.48550/arXiv.2104.03007" } ], "validations": [] @@ -257,36 +277,48 @@ { "name": null, "data": [ + { + "Sub-Sample Size": "1%", + "Sub-Sample K-Marginal Score": 567, + "Deidentified Data K-marginal score": 921, + "Absolute Diff. From Deidentified Data K-marginal Score": "354" + }, + { + "Sub-Sample Size": "5%", + "Sub-Sample K-Marginal Score": 774, + "Deidentified Data K-marginal score": 921, + "Absolute Diff. From Deidentified Data K-marginal Score": "147" + }, { "Sub-Sample Size": "10%", - "Sub-Sample K-Marginal Score": 837, + "Sub-Sample K-Marginal Score": 840, "Deidentified Data K-marginal score": 921, - "Absolute Diff. From Deidentified Data K-marginal Score": "84" + "Absolute Diff. From Deidentified Data K-marginal Score": "81" }, { "Sub-Sample Size": "20%", - "Sub-Sample K-Marginal Score": 889, + "Sub-Sample K-Marginal Score": 886, "Deidentified Data K-marginal score": 921, - "Absolute Diff. From Deidentified Data K-marginal Score": "32" + "Absolute Diff. From Deidentified Data K-marginal Score": "35" }, { "Sub-Sample Size": "30%", - "Sub-Sample K-Marginal Score": 919, + "Sub-Sample K-Marginal Score": 917, "Deidentified Data K-marginal score": 921, - "Absolute Diff. From Deidentified Data K-marginal Score": "2", + "Absolute Diff. From Deidentified Data K-marginal Score": "4", "min_idx": true }, { "Sub-Sample Size": "40%", - "Sub-Sample K-Marginal Score": 932, + "Sub-Sample K-Marginal Score": 933, "Deidentified Data K-marginal score": 921, - "Absolute Diff. From Deidentified Data K-marginal Score": "11" + "Absolute Diff. From Deidentified Data K-marginal Score": "12" }, { "Sub-Sample Size": "50%", - "Sub-Sample K-Marginal Score": 946, + "Sub-Sample K-Marginal Score": 944, "Deidentified Data K-marginal score": 921, - "Absolute Diff. From Deidentified Data K-marginal Score": "25" + "Absolute Diff. From Deidentified Data K-marginal Score": "23" }, { "Sub-Sample Size": "60%", @@ -302,21 +334,15 @@ }, { "Sub-Sample Size": "80%", - "Sub-Sample K-Marginal Score": 972, + "Sub-Sample K-Marginal Score": 973, "Deidentified Data K-marginal score": 921, - "Absolute Diff. From Deidentified Data K-marginal Score": "51" + "Absolute Diff. From Deidentified Data K-marginal Score": "52" }, { "Sub-Sample Size": "90%", - "Sub-Sample K-Marginal Score": 982, + "Sub-Sample K-Marginal Score": 981, "Deidentified Data K-marginal score": 921, - "Absolute Diff. From Deidentified Data K-marginal Score": "61" - }, - { - "Sub-Sample Size": "100%", - "Sub-Sample K-Marginal Score": 1000, - "Deidentified Data K-marginal score": 921, - "Absolute Diff. From Deidentified Data K-marginal Score": "79" + "Absolute Diff. From Deidentified Data K-marginal Score": "60" } ], "type": "table", @@ -337,7 +363,7 @@ "Birmingham City (West)", "https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/" ], - "40% Target Subsample Baseline": 815, + "40% Target Subsample Baseline": 816, "Deidentified Data Score": 785 }, { @@ -346,7 +372,7 @@ "Chicago City (South)--Auburn Gresham, Roseland, Chatham, Avalon Park & Burnside", "https://censusreporter.org/profiles/79500US1703531-chicago-city-south-auburn-gresham-roseland-chatham-avalon-park-burnside-puma-il/" ], - "40% Target Subsample Baseline": 829, + "40% Target Subsample Baseline": 841, "Deidentified Data Score": 806 }, { @@ -355,7 +381,7 @@ "St. Louis City (North)", "https://censusreporter.org/profiles/79500US2901901-st-louis-city-north-puma-mo/" ], - "40% Target Subsample Baseline": 836, + "40% Target Subsample Baseline": 835, "Deidentified Data Score": 809 }, { @@ -373,7 +399,7 @@ "Atlanta Regional Commission--Fulton County (Central)--Atlanta City (Central)", "https://censusreporter.org/profiles/79500US1304600-atlanta-regional-commission-fulton-county-central-atlanta-city-central-puma-ga/" ], - "40% Target Subsample Baseline": 858, + "40% Target Subsample Baseline": 859, "Deidentified Data Score": 817 }, { @@ -391,7 +417,7 @@ "Central Region--Jackson City (East & Central)", "https://censusreporter.org/profiles/79500US2801100-central-region-jackson-city-east-central-puma-ms/" ], - "40% Target Subsample Baseline": 850, + "40% Target Subsample Baseline": 853, "Deidentified Data Score": 823 }, { @@ -400,7 +426,7 @@ "Washtenaw County (East Central)--Ann Arbor City Area", "https://censusreporter.org/profiles/79500US2602702-washtenaw-county-east-central-ann-arbor-city-area-puma-mi/" ], - "40% Target Subsample Baseline": 846, + "40% Target Subsample Baseline": 849, "Deidentified Data Score": 823 }, { @@ -409,7 +435,7 @@ "Las Vegas City (Southeast)", "https://censusreporter.org/profiles/79500US3200405-las-vegas-city-southeast-puma-nv/" ], - "40% Target Subsample Baseline": 849, + "40% Target Subsample Baseline": 857, "Deidentified Data Score": 824 }, { @@ -418,7 +444,7 @@ "Cherokee, Sequoyah & Adair Counties", "https://censusreporter.org/profiles/79500US4000200-cherokee-sequoyah-adair-counties-puma-ok/" ], - "40% Target Subsample Baseline": 854, + "40% Target Subsample Baseline": 858, "Deidentified Data Score": 828 }, { @@ -427,7 +453,7 @@ "Des Moines City", "https://censusreporter.org/profiles/79500US1901700-des-moines-city-puma-ia/" ], - "40% Target Subsample Baseline": 859, + "40% Target Subsample Baseline": 860, "Deidentified Data Score": 829 }, { @@ -436,7 +462,7 @@ "San Francisco County (North & East)--North Beach & Chinatown", "https://censusreporter.org/profiles/79500US0607502-san-francisco-county-north-east-north-beach-chinatown-puma-ca/" ], - "40% Target Subsample Baseline": 842, + "40% Target Subsample Baseline": 850, "Deidentified Data Score": 835 }, { @@ -445,7 +471,7 @@ "Boulder County (Central)--Boulder City", "https://censusreporter.org/profiles/79500US0800803-boulder-county-central-boulder-city-puma-co/" ], - "40% Target Subsample Baseline": 865, + "40% Target Subsample Baseline": 873, "Deidentified Data Score": 845 }, { @@ -454,7 +480,7 @@ "Arlington County (North)", "https://censusreporter.org/profiles/79500US5101301-arlington-county-north-puma-va/" ], - "40% Target Subsample Baseline": 867, + "40% Target Subsample Baseline": 870, "Deidentified Data Score": 850 }, { @@ -463,7 +489,7 @@ "Chicago City (South)--South Shore, Hyde Park, Woodlawn, Grand Boulevard & Douglas", "https://censusreporter.org/profiles/79500US1703529-chicago-city-south-south-shore-hyde-park-woodlawn-grand-boulevard-douglas-puma-il/" ], - "40% Target Subsample Baseline": 871, + "40% Target Subsample Baseline": 866, "Deidentified Data Score": 852 }, { @@ -472,7 +498,7 @@ "East Montana (Outside Billings City)", "https://censusreporter.org/profiles/79500US3000600-east-montana-outside-billings-city-puma-mt/" ], - "40% Target Subsample Baseline": 885, + "40% Target Subsample Baseline": 884, "Deidentified Data Score": 862 }, { @@ -481,7 +507,7 @@ "West North Dakota--Minot City", "https://censusreporter.org/profiles/79500US3800100-west-north-dakota-minot-city-puma-nd/" ], - "40% Target Subsample Baseline": 890, + "40% Target Subsample Baseline": 891, "Deidentified Data Score": 864 }, { @@ -490,7 +516,7 @@ "Santa Clara County (Southwest)--Cupertino, Saratoga Cities & Los Gatos Town", "https://censusreporter.org/profiles/79500US0608507-santa-clara-county-southwest-cupertino-saratoga-cities-los-gatos-town-puma-ca/" ], - "40% Target Subsample Baseline": 885, + "40% Target Subsample Baseline": 883, "Deidentified Data Score": 865 }, { @@ -499,7 +525,7 @@ "Alexandria City", "https://censusreporter.org/profiles/79500US5151255-alexandria-city-puma-va/" ], - "40% Target Subsample Baseline": 875, + "40% Target Subsample Baseline": 876, "Deidentified Data Score": 869 }, { @@ -508,7 +534,7 @@ "Montgomery County (South)--Bethesda, Potomac & North Bethesda", "https://censusreporter.org/profiles/79500US2401004-montgomery-county-south-bethesda-potomac-north-bethesda-puma-md/" ], - "40% Target Subsample Baseline": 907, + "40% Target Subsample Baseline": 906, "Deidentified Data Score": 883 } ], @@ -1453,7 +1479,7 @@ "-1": [ { "name": null, - "data": "This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you\u2019re using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes\u2013 the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.", + "data": "This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you're using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.", "type": "string", "dotted_break": false }, @@ -1520,7 +1546,7 @@ }, { "name": null, - "data": "The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP = \u201cN\u201d, individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. ", + "data": "The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP='N', individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. ", "type": "string", "dotted_break": false }, @@ -1552,6 +1578,12 @@ "metric_name": "Inconsistencies", "attachments": { "-1": [ + { + "name": null, + "data": "In real world tabular data, it's common for record features to have some deterministic, publicly known dependencies on each other: knowing someone's AGEP= 3 necessarily tells you something about their marital status, income and educational attainment. Different deidentification methods may be better or worse at automatically preserving these relationships. When they fail (ex: producing toddlers with PhDs) we say those records contain \"inconsistencies\". Our consistency check metric below is not exhaustive, we don't catch everything the way a production-grade system should, but this will give you a sense of how consistency is preserved for age, work and household features. Note that different deidentification approaches may do better or worse with different types of inconsistencies.", + "type": "string", + "dotted_break": false + }, { "name": "Summary", "data": { @@ -1899,12 +1931,12 @@ } }, { - "metric_name": "K-Marginal Score Breakdown", + "metric_name": "Worst Performing PUMAs Breakdown", "attachments": { "-1": [ { "name": null, - "data": "In the metrics above we\u2019ve considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score.", + "data": "In the metrics above we've considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score.", "type": "string", "dotted_break": false }, @@ -1923,7 +1955,7 @@ "Birmingham City (West)", "https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/" ], - "40% Target Subsample Baseline": 815, + "40% Target Subsample Baseline": 816, "Deidentified Data Score": 785 }, { @@ -1932,7 +1964,7 @@ "Chicago City (South)--Auburn Gresham, Roseland, Chatham, Avalon Park & Burnside", "https://censusreporter.org/profiles/79500US1703531-chicago-city-south-auburn-gresham-roseland-chatham-avalon-park-burnside-puma-il/" ], - "40% Target Subsample Baseline": 829, + "40% Target Subsample Baseline": 841, "Deidentified Data Score": 806 }, { @@ -1941,7 +1973,7 @@ "St. Louis City (North)", "https://censusreporter.org/profiles/79500US2901901-st-louis-city-north-puma-mo/" ], - "40% Target Subsample Baseline": 836, + "40% Target Subsample Baseline": 835, "Deidentified Data Score": 809 }, { @@ -1959,7 +1991,7 @@ "Atlanta Regional Commission--Fulton County (Central)--Atlanta City (Central)", "https://censusreporter.org/profiles/79500US1304600-atlanta-regional-commission-fulton-county-central-atlanta-city-central-puma-ga/" ], - "40% Target Subsample Baseline": 858, + "40% Target Subsample Baseline": 859, "Deidentified Data Score": 817 } ], @@ -2078,13 +2110,31 @@ "-1": [ { "name": null, - "data": "This is a count of unique records in the target data that were exactly reproduced in the deidentified data. Because these records were unique outliers in the target data, and they still appear unchanged in the deidentified data, they are potentially vulnerable to reidentification.", + "data": "Unique Exact Match (UEM) is a simple privacy metric that counts the percentage of singleton records in the target that are also present in the deidentified data; these uniquely identifiable individuals leaked through the deidentification process.", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "Below we also include an estimate of the feature space size. The feature space is the set of all possible record values given the selected target data and feature subset. For instance, if we had two features, Hat [cap, bonnet] and Color [green, blue, purple], our feature space would consist of 2 x 3 = 6 possible combinations (e.g. 'green cap', 'blue bonnet'). Note that feature spaces are based on the feature set, not on what records actually exist in the data. Purple bonnet is a possible combination in this feature space, but it's likely no one in the hypothetical data owns a purple bonnet (and the count of that record value would be 0).", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "As we add features to the feature set, we increase the size of the feature space, but we don't change the actual number of records in the data-- it's the same people, but now they're spread out more thinly across a wider set of possible record values. Large feature spaces will disperse populations very sparsely (most possible record values will have count 0 or 1) and as a result the data will contain very many uniquely identifiable records. Intuitively, once you know enough pieces of information about someone, everyone becomes very distinct from everyone else. This can pose a challenge for privacy. ", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "The Target Data Properties below provides an estimate of the feature space size (100 is used for continuous feature), along with the portion of the records in the ground truth target data that are unique (ie, they are the only person with that record value, they have a count of 1). The Deidentified Data Properties reports the percentage of those uniquely identifiable individuals that are still present in the deidentified data. Because they are unique, real records, they are potentially vulnerable to reidentification.", "type": "string", "dotted_break": false }, { "name": "Target Data Properties", - "data": "Feature space size (possible combinations): -Highlight-3,407,717,673,360,000-Highlight-
Number of unique records in Target Data: -Highlight-27159 (99.66%-Highlight-)", + "data": "Feature space size (possible combinations): -Highlight-5.178e+26-Highlight-
Number of unique records in Target Data: -Highlight-27159 (99.66%-Highlight-)", "type": "string", "dotted_break": false }, @@ -2115,7 +2165,7 @@ }, { "name": null, - "data": "HISP, PUMA, OWN_RENT, EDU, MSP, SEX, RAC1P, INDP_CAT", + "data": "MSP, PUMA, INDP_CAT, EDU, SEX, HISP, OWN_RENT, RAC1P", "type": "string", "dotted_break": false }, @@ -2731,6 +2781,12 @@ "type": "string", "dotted_break": false }, + { + "name": null, + "data": "There are a total of 271 possible codes for INDP, 269 of these codes appear in the Diverse Community Data Excerpts (233 in MA, 264 in Texas and National)", + "type": "string", + "dotted_break": false + }, { "name": "INDP_CAT: Industry categories", "data": [ diff --git a/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/univariate/AGEP.jpg b/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_national2019_06-19-2023T20.33.47/univariate/AGEP.jpg similarity index 100% rename from sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/univariate/AGEP.jpg rename to sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_national2019_06-19-2023T20.33.47/univariate/AGEP.jpg diff --git a/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/univariate/AGEP_counts.csv b/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_national2019_06-19-2023T20.33.47/univariate/AGEP_counts.csv similarity index 100% rename from sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/univariate/AGEP_counts.csv rename to sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_national2019_06-19-2023T20.33.47/univariate/AGEP_counts.csv diff --git a/sdnist/report/sample-reports/report_mostlyai_sd_platform_MichaelPlatzer_2_05-19-2023T18.01.12/univariate/DEAR.jpg 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a/sdnist/report/sample-reports/report_mst_e10_demographic_focused_na2019_05-19-2023T18.01.12/report.html +++ b/sdnist/report/sample-reports/report_mst_e10_demographic_focused_na2019_national2019_06-19-2023T20.33.47/report.html @@ -160,11 +160,11 @@

Data Evaluation Report


-

Report created on: May 19, 2023 18:19:18

+

Report created on: June 19, 2023 20:56:14

Created with - SDNIST v2.2.1 + SDNIST v2.3.0

@@ -230,7 +230,67 @@

- Library + Target Dataset + + + + national2019 + + + + + + + + + + + Epsilon + + + + 10 + + + + + + + + + + + Variant Label + + + + preprocessor-epsilon: 3 + + + + + + + + + + + Algorithm Type + + + + stat model + + + + + + + + + + + Library Name @@ -245,7 +305,7 @@

- Feature Set + Feature Set Name @@ -260,11 +320,11 @@

- Target Dataset + Privacy Category - national2019 + dp @@ -275,11 +335,11 @@

- Epsilon + Deid Data Id - 10 + 506a9dc57d04140f26510caaa6e3aec47e0407b0 @@ -290,11 +350,56 @@

- Variant Label + Features List - preprocessor-epsilon: 3 + AGEP, SEX, MSP, RAC1P, HOUSING_TYPE, OWN_RENT, EDU, PINCP_DECILE, DVET, DEYE + + + + + + + + + + + Privacy Label Detail + + + + Differentially private synthetic data. From McKenna et al. “Winning the NIST Contest: A scalable and general approach to differentially private synthetic data” + + + + + + + + + + + Submission Timestamp + + + + 5/20/2023 00:00:00 + + + + + + + + + + + Team + + + + CRC @@ -305,11 +410,11 @@

- Privacy + Research Papers - Differential Privacy + https://doi.org/10.48550/arXiv.2108.04978 @@ -922,29 +1027,73 @@

- + Sub-Sample Size - + Sub-Sample K-Marginal Score - + Deidentified Data K-marginal score - + Absolute Diff. From Deidentified Data K-marginal Score + + + + + + + + + 1% + + + + 893 + + + + 969 + + + + 76 + + + + + + + + + + + 5% + + + + 953 + + + + 969 + + + + 16 @@ -959,7 +1108,7 @@

- 968 + 969 @@ -967,7 +1116,7 @@

- 1 + 0 @@ -1008,7 +1157,7 @@

- 984 + 985 @@ -1016,7 +1165,7 @@

- 15 + 16 @@ -1031,7 +1180,7 @@

- 987 + 986 @@ -1039,7 +1188,7 @@

- 18 + 17 @@ -1054,7 +1203,7 @@

- 990 + 989 @@ -1062,7 +1211,7 @@

- 21 + 20 @@ -1077,7 +1226,7 @@

- 991 + 990 @@ -1085,7 +1234,7 @@

- 22 + 21 @@ -1159,29 +1308,6 @@

- - - - - - - 100% - - - - 1000 - - - - 969 - - - - 31 - - - -

@@ -3389,7 +3515,7 @@

- This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you’re using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes– the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive. + This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you're using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.
@@ -3672,7 +3798,7 @@

- The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP = “N”, individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. + The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP='N', individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape.
@@ -3782,6 +3908,34 @@

+
+ +
+ + + + +
+ In real world tabular data, it's common for record features to have some deterministic, publicly known dependencies on each other: knowing someone's AGEP= 3 necessarily tells you something about their marital status, income and educational attainment. Different deidentification methods may be better or worse at automatically preserving these relationships. When they fail (ex: producing toddlers with PhDs) we say those records contain "inconsistencies". Our consistency check metric below is not exhaustive, we don't catch everything the way a production-grade system should, but this will give you a sense of how consistency is preserved for age, work and household features. Note that different deidentification approaches may do better or worse with different types of inconsistencies. +
+ + + +
+ + + + + + + + + + + + + +

@@ -7333,6 +7487,7 @@

Privacy Evaluation

+

@@ -7360,7 +7515,91 @@

- This is a count of unique records in the target data that were exactly reproduced in the deidentified data. Because these records were unique outliers in the target data, and they still appear unchanged in the deidentified data, they are potentially vulnerable to reidentification. + Unique Exact Match (UEM) is a simple privacy metric that counts the percentage of singleton records in the target that are also present in the deidentified data; these uniquely identifiable individuals leaked through the deidentification process. +
+ + + +

+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ Below we also include an estimate of the feature space size. The feature space is the set of all possible record values given the selected target data and feature subset. For instance, if we had two features, Hat [cap, bonnet] and Color [green, blue, purple], our feature space would consist of 2 x 3 = 6 possible combinations (e.g. 'green cap', 'blue bonnet'). Note that feature spaces are based on the feature set, not on what records actually exist in the data. Purple bonnet is a possible combination in this feature space, but it's likely no one in the hypothetical data owns a purple bonnet (and the count of that record value would be 0). +
+ + + +
+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ As we add features to the feature set, we increase the size of the feature space, but we don't change the actual number of records in the data-- it's the same people, but now they're spread out more thinly across a wider set of possible record values. Large feature spaces will disperse populations very sparsely (most possible record values will have count 0 or 1) and as a result the data will contain very many uniquely identifiable records. Intuitively, once you know enough pieces of information about someone, everyone becomes very distinct from everyone else. This can pose a challenge for privacy. +
+ + + +
+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ The Target Data Properties below provides an estimate of the feature space size (100 is used for continuous feature), along with the portion of the records in the ground truth target data that are unique (ie, they are the only person with that record value, they have a count of 1). The Deidentified Data Properties reports the percentage of those uniquely identifiable individuals that are still present in the deidentified data. Because they are unique, real records, they are potentially vulnerable to reidentification.
@@ -7404,7 +7643,7 @@

- 227,026,800 + 2.270e+08 @@ -7466,7 +7705,7 @@

- 2027 (7.44%) + 2027 (13.59%) @@ -7493,6 +7732,12 @@

+
+
+
+
+ +

@@ -7583,7 +7828,7 @@

- MSP, OWN_RENT, SEX, EDU, RAC1P + MSP, EDU, SEX, OWN_RENT, RAC1P
@@ -10457,6 +10702,34 @@

+
+ +
+ + + + +
+ There are a total of 271 possible codes for INDP, 269 of these codes appear in the Diverse Community Data Excerpts (233 in MA, 264 in Texas and National) +
+ + + +
+ + + + + + + + + + + + + +

diff --git a/sdnist/report/sample-reports/report_mst_e10_demographic_focused_na2019_05-19-2023T18.01.12/ui.json b/sdnist/report/sample-reports/report_mst_e10_demographic_focused_na2019_national2019_06-19-2023T20.33.47/ui.json similarity index 94% rename from sdnist/report/sample-reports/report_mst_e10_demographic_focused_na2019_05-19-2023T18.01.12/ui.json rename to sdnist/report/sample-reports/report_mst_e10_demographic_focused_na2019_national2019_06-19-2023T20.33.47/ui.json index 3fadf19..5277d15 100644 --- a/sdnist/report/sample-reports/report_mst_e10_demographic_focused_na2019_05-19-2023T18.01.12/ui.json +++ b/sdnist/report/sample-reports/report_mst_e10_demographic_focused_na2019_national2019_06-19-2023T20.33.47/ui.json @@ -1,6 +1,6 @@ { - "Created on": "May 19, 2023 18:19:18", - "version": "2.2.1", + "Created on": "June 19, 2023 20:56:14", + "version": "2.3.0", "data_description": { "target": [ { @@ -42,14 +42,6 @@ "Label Name": "Algorithm Name", "Label Value": "mst" }, - { - "Label Name": "Library", - "Label Value": "smartnoise-synth" - }, - { - "Label Name": "Feature Set", - "Label Value": "demographic-focused" - }, { "Label Name": "Target Dataset", "Label Value": "national2019" @@ -63,8 +55,44 @@ "Label Value": "preprocessor-epsilon: 3" }, { - "Label Name": "Privacy", - "Label Value": "Differential Privacy" + "Label Name": "Algorithm Type", + "Label Value": "stat model" + }, + { + "Label Name": "Library Name", + "Label Value": "smartnoise-synth" + }, + { + "Label Name": "Feature Set Name", + "Label Value": "demographic-focused" + }, + { + "Label Name": "Privacy Category", + "Label Value": "dp" + }, + { + "Label Name": "Deid Data Id", + "Label Value": "506a9dc57d04140f26510caaa6e3aec47e0407b0" + }, + { + "Label Name": "Features List", + "Label Value": "AGEP, SEX, MSP, RAC1P, HOUSING_TYPE, OWN_RENT, EDU, PINCP_DECILE, DVET, DEYE" + }, + { + "Label Name": "Privacy Label Detail", + "Label Value": "Differentially private synthetic data. From McKenna et al. \u201cWinning the NIST Contest: A scalable and general approach to differentially private synthetic data\u201d" + }, + { + "Label Name": "Submission Timestamp", + "Label Value": "5/20/2023 00:00:00" + }, + { + "Label Name": "Team", + "Label Value": "CRC" + }, + { + "Label Name": "Research Papers", + "Label Value": "https://doi.org/10.48550/arXiv.2108.04978" } ], "validations": [] @@ -165,11 +193,23 @@ { "name": null, "data": [ + { + "Sub-Sample Size": "1%", + "Sub-Sample K-Marginal Score": 893, + "Deidentified Data K-marginal score": 969, + "Absolute Diff. From Deidentified Data K-marginal Score": "76" + }, + { + "Sub-Sample Size": "5%", + "Sub-Sample K-Marginal Score": 953, + "Deidentified Data K-marginal score": 969, + "Absolute Diff. From Deidentified Data K-marginal Score": "16" + }, { "Sub-Sample Size": "10%", - "Sub-Sample K-Marginal Score": 968, + "Sub-Sample K-Marginal Score": 969, "Deidentified Data K-marginal score": 969, - "Absolute Diff. From Deidentified Data K-marginal Score": "1", + "Absolute Diff. From Deidentified Data K-marginal Score": "0", "min_idx": true }, { @@ -180,27 +220,27 @@ }, { "Sub-Sample Size": "30%", - "Sub-Sample K-Marginal Score": 984, + "Sub-Sample K-Marginal Score": 985, "Deidentified Data K-marginal score": 969, - "Absolute Diff. From Deidentified Data K-marginal Score": "15" + "Absolute Diff. From Deidentified Data K-marginal Score": "16" }, { "Sub-Sample Size": "40%", - "Sub-Sample K-Marginal Score": 987, + "Sub-Sample K-Marginal Score": 986, "Deidentified Data K-marginal score": 969, - "Absolute Diff. From Deidentified Data K-marginal Score": "18" + "Absolute Diff. From Deidentified Data K-marginal Score": "17" }, { "Sub-Sample Size": "50%", - "Sub-Sample K-Marginal Score": 990, + "Sub-Sample K-Marginal Score": 989, "Deidentified Data K-marginal score": 969, - "Absolute Diff. From Deidentified Data K-marginal Score": "21" + "Absolute Diff. From Deidentified Data K-marginal Score": "20" }, { "Sub-Sample Size": "60%", - "Sub-Sample K-Marginal Score": 991, + "Sub-Sample K-Marginal Score": 990, "Deidentified Data K-marginal score": 969, - "Absolute Diff. From Deidentified Data K-marginal Score": "22" + "Absolute Diff. From Deidentified Data K-marginal Score": "21" }, { "Sub-Sample Size": "70%", @@ -219,12 +259,6 @@ "Sub-Sample K-Marginal Score": 996, "Deidentified Data K-marginal score": 969, "Absolute Diff. From Deidentified Data K-marginal Score": "27" - }, - { - "Sub-Sample Size": "100%", - "Sub-Sample K-Marginal Score": 1000, - "Deidentified Data K-marginal score": 969, - "Absolute Diff. From Deidentified Data K-marginal Score": "31" } ], "type": "table", @@ -899,7 +933,7 @@ "-1": [ { "name": null, - "data": "This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you\u2019re using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes\u2013 the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.", + "data": "This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you're using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.", "type": "string", "dotted_break": false }, @@ -966,7 +1000,7 @@ }, { "name": null, - "data": "The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP = \u201cN\u201d, individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. ", + "data": "The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP='N', individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. ", "type": "string", "dotted_break": false }, @@ -998,6 +1032,12 @@ "metric_name": "Inconsistencies", "attachments": { "-1": [ + { + "name": null, + "data": "In real world tabular data, it's common for record features to have some deterministic, publicly known dependencies on each other: knowing someone's AGEP= 3 necessarily tells you something about their marital status, income and educational attainment. Different deidentification methods may be better or worse at automatically preserving these relationships. When they fail (ex: producing toddlers with PhDs) we say those records contain \"inconsistencies\". Our consistency check metric below is not exhaustive, we don't catch everything the way a production-grade system should, but this will give you a sense of how consistency is preserved for age, work and household features. Note that different deidentification approaches may do better or worse with different types of inconsistencies.", + "type": "string", + "dotted_break": false + }, { "name": "Summary", "data": { @@ -1517,19 +1557,37 @@ "-1": [ { "name": null, - "data": "This is a count of unique records in the target data that were exactly reproduced in the deidentified data. Because these records were unique outliers in the target data, and they still appear unchanged in the deidentified data, they are potentially vulnerable to reidentification.", + "data": "Unique Exact Match (UEM) is a simple privacy metric that counts the percentage of singleton records in the target that are also present in the deidentified data; these uniquely identifiable individuals leaked through the deidentification process.", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "Below we also include an estimate of the feature space size. The feature space is the set of all possible record values given the selected target data and feature subset. For instance, if we had two features, Hat [cap, bonnet] and Color [green, blue, purple], our feature space would consist of 2 x 3 = 6 possible combinations (e.g. 'green cap', 'blue bonnet'). Note that feature spaces are based on the feature set, not on what records actually exist in the data. Purple bonnet is a possible combination in this feature space, but it's likely no one in the hypothetical data owns a purple bonnet (and the count of that record value would be 0).", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "As we add features to the feature set, we increase the size of the feature space, but we don't change the actual number of records in the data-- it's the same people, but now they're spread out more thinly across a wider set of possible record values. Large feature spaces will disperse populations very sparsely (most possible record values will have count 0 or 1) and as a result the data will contain very many uniquely identifiable records. Intuitively, once you know enough pieces of information about someone, everyone becomes very distinct from everyone else. This can pose a challenge for privacy. ", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "The Target Data Properties below provides an estimate of the feature space size (100 is used for continuous feature), along with the portion of the records in the ground truth target data that are unique (ie, they are the only person with that record value, they have a count of 1). The Deidentified Data Properties reports the percentage of those uniquely identifiable individuals that are still present in the deidentified data. Because they are unique, real records, they are potentially vulnerable to reidentification.", "type": "string", "dotted_break": false }, { "name": "Target Data Properties", - "data": "Feature space size (possible combinations): -Highlight-227,026,800-Highlight-
Number of unique records in Target Data: -Highlight-14918 (54.74%-Highlight-)", + "data": "Feature space size (possible combinations): -Highlight-2.270e+08-Highlight-
Number of unique records in Target Data: -Highlight-14918 (54.74%-Highlight-)", "type": "string", "dotted_break": false }, { "name": "Deidentified Data Properties", - "data": "Number of unique Target Data records exactly matched in Deid. Data: -Highlight-2027 (7.44%)-Highlight-", + "data": "Number of unique Target Data records exactly matched in Deid. Data: -Highlight-2027 (13.59%)-Highlight-", "type": "string", "dotted_break": false } @@ -1554,7 +1612,7 @@ }, { "name": null, - "data": "MSP, OWN_RENT, SEX, EDU, RAC1P", + "data": "MSP, EDU, SEX, OWN_RENT, RAC1P", "type": "string", "dotted_break": false }, @@ -2170,6 +2228,12 @@ "type": "string", "dotted_break": false }, + { + "name": null, + "data": "There are a total of 271 possible codes for INDP, 269 of these codes appear in the Diverse Community Data Excerpts (233 in MA, 264 in Texas and National)", + "type": "string", + "dotted_break": false + }, { "name": "INDP_CAT: Industry categories", "data": [ diff --git a/sdnist/report/sample-reports/report_mst_e10_demographic_focused_na2019_05-19-2023T18.01.12/univariate/AGEP.jpg b/sdnist/report/sample-reports/report_mst_e10_demographic_focused_na2019_national2019_06-19-2023T20.33.47/univariate/AGEP.jpg similarity index 100% rename from sdnist/report/sample-reports/report_mst_e10_demographic_focused_na2019_05-19-2023T18.01.12/univariate/AGEP.jpg rename to sdnist/report/sample-reports/report_mst_e10_demographic_focused_na2019_national2019_06-19-2023T20.33.47/univariate/AGEP.jpg diff --git a/sdnist/report/sample-reports/report_mst_e10_demographic_focused_na2019_05-19-2023T18.01.12/univariate/AGEP_counts.csv b/sdnist/report/sample-reports/report_mst_e10_demographic_focused_na2019_national2019_06-19-2023T20.33.47/univariate/AGEP_counts.csv similarity index 100% rename from sdnist/report/sample-reports/report_mst_e10_demographic_focused_na2019_05-19-2023T18.01.12/univariate/AGEP_counts.csv rename to sdnist/report/sample-reports/report_mst_e10_demographic_focused_na2019_national2019_06-19-2023T20.33.47/univariate/AGEP_counts.csv diff --git a/sdnist/report/sample-reports/report_mst_e10_demographic_focused_na2019_05-19-2023T18.01.12/univariate/DEYE.jpg 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deleted file mode 100644 index dbb9f3d..0000000 --- a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv +++ /dev/null @@ -1,6 +0,0 @@ -,PUMA,40% Target Subsample Baseline,Deidentified Data Score -0,24-01004,904,2 -1,51-51255,871,4 -2,36-03710,821,98 -3,01-01301,809,108 -4,38-00100,890,159 diff --git a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/k_marginal_synopsys/subsample_error_comparison.csv b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/k_marginal_synopsys/subsample_error_comparison.csv deleted file mode 100644 index ae7d589..0000000 --- a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/k_marginal_synopsys/subsample_error_comparison.csv +++ /dev/null @@ -1,11 +0,0 @@ -,Sub-Sample Size,Sub-Sample K-Marginal Score,Deidentified Data K-marginal score,Absolute Diff. From Deidentified Data K-marginal Score -0,10%,835,571,264 -1,20%,886,571,315 -2,30%,912,571,341 -3,40%,929,571,358 -4,50%,944,571,373 -5,60%,954,571,383 -6,70%,963,571,392 -7,80%,971,571,400 -8,90%,981,571,410 -9,100%,1000,571,429 diff --git a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/pca/components_eigenvector.csv b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/pca/components_eigenvector.csv deleted file mode 100644 index 9c1b67f..0000000 --- a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/pca/components_eigenvector.csv +++ /dev/null @@ -1,6 +0,0 @@ -,AGEP,HISP,MSP,NOC,NPF,OWN_RENT,PINCP_DECILE,POVPIP,PUMA,RAC1P,SEX -0,-0.37712970483181446,0.10413523716016826,-0.33147797089484315,0.5079493510979781,0.4785791057231923,0.19554130976462414,-0.3508381593954948,-0.25103327879987963,0.08098020901853498,0.13530945076500997,0.028158879848405823 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b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/k_marginal_breakdown/univariate/RAC1P_counts.csv similarity index 100% rename from sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/k_marginal_breakdown/univariate/RAC1P_counts.csv rename to sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/k_marginal_breakdown/univariate/RAC1P_counts.csv diff --git a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/k_marginal_breakdown/univariate/SEX.jpg b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/k_marginal_breakdown/univariate/SEX.jpg similarity index 100% rename from sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/k_marginal_breakdown/univariate/SEX.jpg rename to 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b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/k_marginal_breakdown/univariate/divergence.csv similarity index 100% rename from sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/k_marginal_breakdown/univariate/divergence.csv rename to sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/k_marginal_breakdown/univariate/divergence.csv diff --git a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv new file mode 100644 index 0000000..6133cc7 --- /dev/null +++ b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv @@ -0,0 +1,6 @@ +,PUMA,40% Target Subsample Baseline,Deidentified Data Score +0,24-01004,899,2 +1,51-51255,877,4 +2,36-03710,807,98 +3,01-01301,801,108 +4,38-00100,888,159 diff --git a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv similarity index 83% rename from sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv rename to sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv index 8ce5b0f..a750cd8 100644 --- a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv +++ b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv @@ -1,21 +1,21 @@ ,PUMA,40% Target Subsample Baseline,Deidentified Data Score -0,"['24-01004', 'Montgomery County (South)--Bethesda, Potomac & North Bethesda', 'https://censusreporter.org/profiles/79500US2401004-montgomery-county-south-bethesda-potomac-north-bethesda-puma-md/']",904,2 -1,"['51-51255', 'Alexandria City', 'https://censusreporter.org/profiles/79500US5151255-alexandria-city-puma-va/']",871,4 -2,"['36-03710', 'NYC-Bronx Community District 1 & 2--Hunts Point, Longwood & Melrose', 'https://censusreporter.org/profiles/79500US3603710-nyc-bronx-community-district-1-2-hunts-point-longwood-melrose-puma-ny/']",821,98 -3,"['01-01301', 'Birmingham City (West)', 'https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/']",809,108 -4,"['38-00100', 'West North Dakota--Minot City', 'https://censusreporter.org/profiles/79500US3800100-west-north-dakota-minot-city-puma-nd/']",890,159 -5,"['32-00405', 'Las Vegas City (Southeast)', 'https://censusreporter.org/profiles/79500US3200405-las-vegas-city-southeast-puma-nv/']",841,170 -6,"['40-00200', 'Cherokee, Sequoyah & Adair Counties', 'https://censusreporter.org/profiles/79500US4000200-cherokee-sequoyah-adair-counties-puma-ok/']",834,194 -7,"['17-03531', 'Chicago City (South)--Auburn Gresham, Roseland, Chatham, Avalon Park & Burnside', 'https://censusreporter.org/profiles/79500US1703531-chicago-city-south-auburn-gresham-roseland-chatham-avalon-park-burnside-puma-il/']",833,226 +0,"['24-01004', 'Montgomery County (South)--Bethesda, Potomac & North Bethesda', 'https://censusreporter.org/profiles/79500US2401004-montgomery-county-south-bethesda-potomac-north-bethesda-puma-md/']",899,2 +1,"['51-51255', 'Alexandria City', 'https://censusreporter.org/profiles/79500US5151255-alexandria-city-puma-va/']",877,4 +2,"['36-03710', 'NYC-Bronx Community District 1 & 2--Hunts Point, Longwood & Melrose', 'https://censusreporter.org/profiles/79500US3603710-nyc-bronx-community-district-1-2-hunts-point-longwood-melrose-puma-ny/']",807,98 +3,"['01-01301', 'Birmingham City (West)', 'https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/']",801,108 +4,"['38-00100', 'West North Dakota--Minot City', 'https://censusreporter.org/profiles/79500US3800100-west-north-dakota-minot-city-puma-nd/']",888,159 +5,"['32-00405', 'Las Vegas City (Southeast)', 'https://censusreporter.org/profiles/79500US3200405-las-vegas-city-southeast-puma-nv/']",843,170 +6,"['40-00200', 'Cherokee, Sequoyah & Adair Counties', 'https://censusreporter.org/profiles/79500US4000200-cherokee-sequoyah-adair-counties-puma-ok/']",836,194 +7,"['17-03531', 'Chicago City (South)--Auburn Gresham, Roseland, Chatham, Avalon Park & Burnside', 'https://censusreporter.org/profiles/79500US1703531-chicago-city-south-auburn-gresham-roseland-chatham-avalon-park-burnside-puma-il/']",834,226 8,"['30-00600', 'East Montana (Outside Billings City)', 'https://censusreporter.org/profiles/79500US3000600-east-montana-outside-billings-city-puma-mt/']",880,234 9,"['17-03529', 'Chicago City (South)--South Shore, Hyde Park, Woodlawn, Grand Boulevard & Douglas', 'https://censusreporter.org/profiles/79500US1703529-chicago-city-south-south-shore-hyde-park-woodlawn-grand-boulevard-douglas-puma-il/']",867,250 -10,"['13-04600', 'Atlanta Regional Commission--Fulton County (Central)--Atlanta City (Central)', 'https://censusreporter.org/profiles/79500US1304600-atlanta-regional-commission-fulton-county-central-atlanta-city-central-puma-ga/']",858,263 -11,"['36-04010', 'NYC-Brooklyn Community District 17--East Flatbush, Farragut & Rugby', 'https://censusreporter.org/profiles/79500US3604010-nyc-brooklyn-community-district-17-east-flatbush-farragut-rugby-puma-ny/']",837,277 -12,"['28-01100', 'Central Region--Jackson City (East & Central)', 'https://censusreporter.org/profiles/79500US2801100-central-region-jackson-city-east-central-puma-ms/']",853,281 -13,"['06-08507', 'Santa Clara County (Southwest)--Cupertino, Saratoga Cities & Los Gatos Town', 'https://censusreporter.org/profiles/79500US0608507-santa-clara-county-southwest-cupertino-saratoga-cities-los-gatos-town-puma-ca/']",879,321 -14,"['29-01901', 'St. Louis City (North)', 'https://censusreporter.org/profiles/79500US2901901-st-louis-city-north-puma-mo/']",843,321 -15,"['19-01700', 'Des Moines City', 'https://censusreporter.org/profiles/79500US1901700-des-moines-city-puma-ia/']",847,327 -16,"['26-02702', 'Washtenaw County (East Central)--Ann Arbor City Area', 'https://censusreporter.org/profiles/79500US2602702-washtenaw-county-east-central-ann-arbor-city-area-puma-mi/']",843,333 -17,"['51-01301', 'Arlington County (North)', 'https://censusreporter.org/profiles/79500US5101301-arlington-county-north-puma-va/']",864,371 -18,"['06-07502', 'San Francisco County (North & East)--North Beach & Chinatown', 'https://censusreporter.org/profiles/79500US0607502-san-francisco-county-north-east-north-beach-chinatown-puma-ca/']",857,413 -19,"['08-00803', 'Boulder County (Central)--Boulder City', 'https://censusreporter.org/profiles/79500US0800803-boulder-county-central-boulder-city-puma-co/']",866,425 +10,"['13-04600', 'Atlanta Regional Commission--Fulton County (Central)--Atlanta City (Central)', 'https://censusreporter.org/profiles/79500US1304600-atlanta-regional-commission-fulton-county-central-atlanta-city-central-puma-ga/']",855,263 +11,"['36-04010', 'NYC-Brooklyn Community District 17--East Flatbush, Farragut & Rugby', 'https://censusreporter.org/profiles/79500US3604010-nyc-brooklyn-community-district-17-east-flatbush-farragut-rugby-puma-ny/']",843,277 +12,"['28-01100', 'Central Region--Jackson City (East & Central)', 'https://censusreporter.org/profiles/79500US2801100-central-region-jackson-city-east-central-puma-ms/']",850,281 +13,"['06-08507', 'Santa Clara County (Southwest)--Cupertino, Saratoga Cities & Los Gatos Town', 'https://censusreporter.org/profiles/79500US0608507-santa-clara-county-southwest-cupertino-saratoga-cities-los-gatos-town-puma-ca/']",886,321 +14,"['29-01901', 'St. Louis City (North)', 'https://censusreporter.org/profiles/79500US2901901-st-louis-city-north-puma-mo/']",838,321 +15,"['19-01700', 'Des Moines City', 'https://censusreporter.org/profiles/79500US1901700-des-moines-city-puma-ia/']",850,327 +16,"['26-02702', 'Washtenaw County (East Central)--Ann Arbor City Area', 'https://censusreporter.org/profiles/79500US2602702-washtenaw-county-east-central-ann-arbor-city-area-puma-mi/']",852,333 +17,"['51-01301', 'Arlington County (North)', 'https://censusreporter.org/profiles/79500US5101301-arlington-county-north-puma-va/']",869,371 +18,"['06-07502', 'San Francisco County (North & East)--North Beach & Chinatown', 'https://censusreporter.org/profiles/79500US0607502-san-francisco-county-north-east-north-beach-chinatown-puma-ca/']",859,413 +19,"['08-00803', 'Boulder County (Central)--Boulder City', 'https://censusreporter.org/profiles/79500US0800803-boulder-county-central-boulder-city-puma-co/']",868,425 diff --git a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/k_marginal_synopsys/subsample_error_comparison.csv b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/k_marginal_synopsys/subsample_error_comparison.csv new file mode 100644 index 0000000..ab33f85 --- /dev/null +++ b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/k_marginal_synopsys/subsample_error_comparison.csv @@ -0,0 +1,12 @@ +,Sub-Sample Size,Sub-Sample K-Marginal Score,Deidentified Data K-marginal score,Absolute Diff. From Deidentified Data K-marginal Score +0,1%,524,571,47 +1,5%,764,571,193 +2,10%,828,571,257 +3,20%,889,571,318 +4,30%,913,571,342 +5,40%,934,571,363 +6,50%,944,571,373 +7,60%,953,571,382 +8,70%,963,571,392 +9,80%,972,571,401 +10,90%,981,571,410 diff --git a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/pca/components_eigenvector.csv b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/pca/components_eigenvector.csv new file mode 100644 index 0000000..45fb07a --- /dev/null +++ b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/pca/components_eigenvector.csv @@ -0,0 +1,6 @@ +,AGEP,HISP,MSP,NOC,NPF,OWN_RENT,PINCP_DECILE,POVPIP,PUMA,RAC1P,SEX +0,-0.3771297048318147,0.10413523716016826,-0.33147797089484365,0.5079493510979788,0.4785791057231925,0.1955413097646237,-0.3508381593954956,-0.2510332787998792,0.08098020901853503,0.13530945076501003,0.028158879848405385 +1,0.052178973524244,0.24807681582745744,0.3832195882398835,-0.12304575686134048,-0.2486302288219401,0.5784354225419197,-0.06139564421859421,-0.5542800656460337,-0.058284657287332044,0.22155712006781622,0.11769763512410059 +2,-0.43936346916089103,0.31840414482309953,0.26410643349457386,-0.1666864120736697,-0.15586776602492627,-0.3064122138005875,-0.3998766337843848,0.29831913007042804,-0.19432893443511368,0.44505361627842355,-0.07160088936239073 +3,0.17623700639528622,0.6169614406085188,-0.10171802336394213,-0.01807741156172793,0.08682112931289068,-0.024411995427564254,0.30000688005160664,0.1059673167284719,0.603570249980386,0.22463869077169948,-0.23129873958019645 +4,0.046704946659398804,0.1264815427285109,-0.0004449587316844878,-0.09334062214149583,-0.049240919725071344,-0.19457502251121944,-0.23857217577583748,0.007353049551725823,0.3555128272420255,-0.15737767160478092,0.8512910417538901 diff --git a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/pca/deidentified.png b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/pca/deidentified.png similarity index 100% rename from sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/pca/deidentified.png rename to sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/pca/deidentified.png diff --git a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/pca/deidentified_highlighted_MSP/MSP_N.png b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/pca/deidentified_highlighted_MSP/MSP_N.png similarity index 100% rename from sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/pca/deidentified_highlighted_MSP/MSP_N.png rename to sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/pca/deidentified_highlighted_MSP/MSP_N.png diff --git a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/pca/target.png b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/pca/target.png similarity index 100% rename from sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/pca/target.png rename to sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/pca/target.png diff --git a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/pca/target_highlighted_MSP/MSP_N.png b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/pca/target_highlighted_MSP/MSP_N.png similarity index 100% rename from sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/pca/target_highlighted_MSP/MSP_N.png rename to sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/pca/target_highlighted_MSP/MSP_N.png diff --git a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/pearson_correlation/correlation_difference.csv b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/pearson_correlation/correlation_difference.csv similarity index 100% rename from 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a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/propensity/propensity_distribution.csv b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/propensity/propensity_distribution.csv similarity index 100% rename from sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/propensity/propensity_distribution.csv rename to sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/propensity/propensity_distribution.csv diff --git a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/propensity/propensity_distribution.jpg b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/propensity/propensity_distribution.jpg new file mode 100644 index 0000000..deb373a Binary files /dev/null and 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Data Evaluation Report


-

Report created on: May 19, 2023 18:14:41

+

Report created on: June 19, 2023 20:51:55

Created with - SDNIST v2.2.1 + SDNIST v2.3.0

@@ -230,7 +230,67 @@

- Library + Target Dataset + + + + national2019 + + + + + + + + + + + Epsilon + + + + 10 + + + + + + + + + + + Variant Label + + + + preprocessor-epsilon: 3 + + + + + + + + + + + Algorithm Type + + + + query matching + + + + + + + + + + + Library Name @@ -245,7 +305,7 @@

- Feature Set + Feature Set Name @@ -260,11 +320,11 @@

- Target Dataset + Privacy Category - national2019 + dp @@ -275,11 +335,11 @@

- Epsilon + Deid Data Id - 10 + 44e2092e155fe2b9b97c2286bc404ed42a8f0cd8 @@ -290,11 +350,56 @@

- Variant Label + Features List - preprocessor-epsilon: 3 + PUMA, AGEP, SEX, MSP, HISP, RAC1P, NOC, NPF, OWN_RENT, PINCP_DECILE, POVPIP + + + + + + + + + + + Privacy Label Detail + + + + The pac-synth synthesizer will suppress marginal combinations that could uniquely fingerprint individuals (similar to k-anonymity). pac-synth is a differentially-private synthesizer that computes differentially private marginals to build synthetic data. + + + + + + + + + + + Submission Timestamp + + + + 5/20/2023 00:00:00 + + + + + + + + + + + Team + + + + CRC @@ -305,11 +410,11 @@

- Privacy + Research Papers - Differential Privacy + https://github.com/microsoft/synthetic-data-showcase/tree/main/docs/dp @@ -913,7 +1018,7 @@

- K-Marginal score of the deidentified data closely resembles K-Marginal score of a 10% sub-sample of the target data. + K-Marginal score of the deidentified data closely resembles K-Marginal score of a 1% sub-sample of the target data.
@@ -978,11 +1083,11 @@

- 10% + 1% - 835 + 524 @@ -990,7 +1095,7 @@

- 264 + 47 @@ -1004,11 +1109,11 @@

- 20% + 5% - 886 + 764 @@ -1016,7 +1121,7 @@

- 315 + 193 @@ -1027,11 +1132,11 @@

- 30% + 10% - 912 + 828 @@ -1039,7 +1144,7 @@

- 341 + 257 @@ -1050,11 +1155,11 @@

- 40% + 20% - 929 + 889 @@ -1062,7 +1167,7 @@

- 358 + 318 @@ -1073,11 +1178,11 @@

- 50% + 30% - 944 + 913 @@ -1085,7 +1190,7 @@

- 373 + 342 @@ -1096,11 +1201,11 @@

- 60% + 40% - 954 + 934 @@ -1108,7 +1213,7 @@

- 383 + 363 @@ -1119,11 +1224,11 @@

- 70% + 50% - 963 + 944 @@ -1131,7 +1236,7 @@

- 392 + 373 @@ -1142,11 +1247,11 @@

- 80% + 60% - 971 + 953 @@ -1154,7 +1259,7 @@

- 400 + 382 @@ -1165,11 +1270,11 @@

- 90% + 70% - 981 + 963 @@ -1177,7 +1282,7 @@

- 410 + 392 @@ -1188,11 +1293,11 @@

- 100% + 80% - 1000 + 972 @@ -1200,7 +1305,30 @@

- 429 + 401 + + + + + + + + + + + 90% + + + + 981 + + + + 571 + + + + 410 @@ -1304,7 +1432,7 @@

- 904 + 899 @@ -1326,7 +1454,7 @@

- 871 + 877 @@ -1348,7 +1476,7 @@

- 821 + 807 @@ -1370,7 +1498,7 @@

- 809 + 801 @@ -1392,7 +1520,7 @@

- 890 + 888 @@ -1414,7 +1542,7 @@

- 841 + 843 @@ -1436,7 +1564,7 @@

- 834 + 836 @@ -1458,7 +1586,7 @@

- 833 + 834 @@ -1524,7 +1652,7 @@

- 858 + 855 @@ -1546,7 +1674,7 @@

- 837 + 843 @@ -1568,7 +1696,7 @@

- 853 + 850 @@ -1590,7 +1718,7 @@

- 879 + 886 @@ -1612,7 +1740,7 @@

- 843 + 838 @@ -1634,7 +1762,7 @@

- 847 + 850 @@ -1656,7 +1784,7 @@

- 843 + 852 @@ -1678,7 +1806,7 @@

- 864 + 869 @@ -1700,7 +1828,7 @@

- 857 + 859 @@ -1722,7 +1850,7 @@

- 866 + 868 @@ -2872,7 +3000,7 @@

- This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you’re using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes– the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive. + This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you're using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.
@@ -3155,7 +3283,7 @@

- The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP = “N”, individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. + The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP='N', individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape.
@@ -3265,6 +3393,34 @@

+
+ +
+ + + + +
+ In real world tabular data, it's common for record features to have some deterministic, publicly known dependencies on each other: knowing someone's AGEP= 3 necessarily tells you something about their marital status, income and educational attainment. Different deidentification methods may be better or worse at automatically preserving these relationships. When they fail (ex: producing toddlers with PhDs) we say those records contain "inconsistencies". Our consistency check metric below is not exhaustive, we don't catch everything the way a production-grade system should, but this will give you a sense of how consistency is preserved for age, work and household features. Note that different deidentification approaches may do better or worse with different types of inconsistencies. +
+ + + +
+ + + + + + + + + + + + + +

@@ -3532,7 +3688,7 @@

- K-Marginal Score Breakdown: + Worst Performing PUMAs Breakdown:

@@ -3556,7 +3712,7 @@

- In the metrics above we’ve considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score. + In the metrics above we've considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score.
@@ -3655,7 +3811,7 @@

- 904 + 899 @@ -3677,7 +3833,7 @@

- 871 + 877 @@ -3699,7 +3855,7 @@

- 821 + 807 @@ -3721,7 +3877,7 @@

- 809 + 801 @@ -3743,7 +3899,7 @@

- 890 + 888 @@ -4223,6 +4379,7 @@

Privacy Evaluation

+

@@ -4250,7 +4407,91 @@

- This is a count of unique records in the target data that were exactly reproduced in the deidentified data. Because these records were unique outliers in the target data, and they still appear unchanged in the deidentified data, they are potentially vulnerable to reidentification. + Unique Exact Match (UEM) is a simple privacy metric that counts the percentage of singleton records in the target that are also present in the deidentified data; these uniquely identifiable individuals leaked through the deidentification process. +
+ + + +

+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ Below we also include an estimate of the feature space size. The feature space is the set of all possible record values given the selected target data and feature subset. For instance, if we had two features, Hat [cap, bonnet] and Color [green, blue, purple], our feature space would consist of 2 x 3 = 6 possible combinations (e.g. 'green cap', 'blue bonnet'). Note that feature spaces are based on the feature set, not on what records actually exist in the data. Purple bonnet is a possible combination in this feature space, but it's likely no one in the hypothetical data owns a purple bonnet (and the count of that record value would be 0). +
+ + + +
+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ As we add features to the feature set, we increase the size of the feature space, but we don't change the actual number of records in the data-- it's the same people, but now they're spread out more thinly across a wider set of possible record values. Large feature spaces will disperse populations very sparsely (most possible record values will have count 0 or 1) and as a result the data will contain very many uniquely identifiable records. Intuitively, once you know enough pieces of information about someone, everyone becomes very distinct from everyone else. This can pose a challenge for privacy. +
+ + + +
+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ The Target Data Properties below provides an estimate of the feature space size (100 is used for continuous feature), along with the portion of the records in the ground truth target data that are unique (ie, they are the only person with that record value, they have a count of 1). The Deidentified Data Properties reports the percentage of those uniquely identifiable individuals that are still present in the deidentified data. Because they are unique, real records, they are potentially vulnerable to reidentification.
@@ -4294,7 +4535,7 @@

- 64,449,000 + 4.990e+11 @@ -4356,7 +4597,7 @@

- 208 (0.76%) + 208 (0.87%) @@ -4383,6 +4624,12 @@

+
+
+
+
+ +

@@ -4473,7 +4720,7 @@

- PUMA, SEX, HISP, MSP, RAC1P, OWN_RENT + SEX, MSP, RAC1P, OWN_RENT, HISP, PUMA
@@ -7347,6 +7594,34 @@

+
+ +
+ + + + +
+ There are a total of 271 possible codes for INDP, 269 of these codes appear in the Diverse Community Data Excerpts (233 in MA, 264 in Texas and National) +
+ + + +
+ + + + + + + + + + + + + +

diff --git a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/ui.json b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/ui.json similarity index 92% rename from sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/ui.json rename to sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/ui.json index fff5aff..794920f 100644 --- a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/ui.json +++ b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/ui.json @@ -1,6 +1,6 @@ { - "Created on": "May 19, 2023 18:14:41", - "version": "2.2.1", + "Created on": "June 19, 2023 20:51:55", + "version": "2.3.0", "data_description": { "target": [ { @@ -42,14 +42,6 @@ "Label Name": "Algorithm Name", "Label Value": "pacsynth" }, - { - "Label Name": "Library", - "Label Value": "smartnoise-synth" - }, - { - "Label Name": "Feature Set", - "Label Value": "family-focused" - }, { "Label Name": "Target Dataset", "Label Value": "national2019" @@ -63,8 +55,44 @@ "Label Value": "preprocessor-epsilon: 3" }, { - "Label Name": "Privacy", - "Label Value": "Differential Privacy" + "Label Name": "Algorithm Type", + "Label Value": "query matching" + }, + { + "Label Name": "Library Name", + "Label Value": "smartnoise-synth" + }, + { + "Label Name": "Feature Set Name", + "Label Value": "family-focused" + }, + { + "Label Name": "Privacy Category", + "Label Value": "dp" + }, + { + "Label Name": "Deid Data Id", + "Label Value": "44e2092e155fe2b9b97c2286bc404ed42a8f0cd8" + }, + { + "Label Name": "Features List", + "Label Value": "PUMA, AGEP, SEX, MSP, HISP, RAC1P, NOC, NPF, OWN_RENT, PINCP_DECILE, POVPIP" + }, + { + "Label Name": "Privacy Label Detail", + "Label Value": "The pac-synth synthesizer will suppress marginal combinations that could uniquely fingerprint individuals (similar to k-anonymity). pac-synth is a differentially-private synthesizer that computes differentially private marginals to build synthetic data. " + }, + { + "Label Name": "Submission Timestamp", + "Label Value": "5/20/2023 00:00:00" + }, + { + "Label Name": "Team", + "Label Value": "CRC" + }, + { + "Label Name": "Research Papers", + "Label Value": "https://github.com/microsoft/synthetic-data-showcase/tree/main/docs/dp" } ], "validations": [] @@ -164,7 +192,7 @@ }, { "name": null, - "data": "K-Marginal score of the deidentified data closely resembles K-Marginal score of a 10% sub-sample of the target data.", + "data": "K-Marginal score of the deidentified data closely resembles K-Marginal score of a 1% sub-sample of the target data.", "type": "string", "dotted_break": false }, @@ -172,29 +200,41 @@ "name": null, "data": [ { - "Sub-Sample Size": "10%", - "Sub-Sample K-Marginal Score": 835, + "Sub-Sample Size": "1%", + "Sub-Sample K-Marginal Score": 524, "Deidentified Data K-marginal score": 571, - "Absolute Diff. From Deidentified Data K-marginal Score": "264", + "Absolute Diff. From Deidentified Data K-marginal Score": "47", "min_idx": true }, + { + "Sub-Sample Size": "5%", + "Sub-Sample K-Marginal Score": 764, + "Deidentified Data K-marginal score": 571, + "Absolute Diff. From Deidentified Data K-marginal Score": "193" + }, + { + "Sub-Sample Size": "10%", + "Sub-Sample K-Marginal Score": 828, + "Deidentified Data K-marginal score": 571, + "Absolute Diff. From Deidentified Data K-marginal Score": "257" + }, { "Sub-Sample Size": "20%", - "Sub-Sample K-Marginal Score": 886, + "Sub-Sample K-Marginal Score": 889, "Deidentified Data K-marginal score": 571, - "Absolute Diff. From Deidentified Data K-marginal Score": "315" + "Absolute Diff. From Deidentified Data K-marginal Score": "318" }, { "Sub-Sample Size": "30%", - "Sub-Sample K-Marginal Score": 912, + "Sub-Sample K-Marginal Score": 913, "Deidentified Data K-marginal score": 571, - "Absolute Diff. From Deidentified Data K-marginal Score": "341" + "Absolute Diff. From Deidentified Data K-marginal Score": "342" }, { "Sub-Sample Size": "40%", - "Sub-Sample K-Marginal Score": 929, + "Sub-Sample K-Marginal Score": 934, "Deidentified Data K-marginal score": 571, - "Absolute Diff. From Deidentified Data K-marginal Score": "358" + "Absolute Diff. From Deidentified Data K-marginal Score": "363" }, { "Sub-Sample Size": "50%", @@ -204,9 +244,9 @@ }, { "Sub-Sample Size": "60%", - "Sub-Sample K-Marginal Score": 954, + "Sub-Sample K-Marginal Score": 953, "Deidentified Data K-marginal score": 571, - "Absolute Diff. From Deidentified Data K-marginal Score": "383" + "Absolute Diff. From Deidentified Data K-marginal Score": "382" }, { "Sub-Sample Size": "70%", @@ -216,21 +256,15 @@ }, { "Sub-Sample Size": "80%", - "Sub-Sample K-Marginal Score": 971, + "Sub-Sample K-Marginal Score": 972, "Deidentified Data K-marginal score": 571, - "Absolute Diff. From Deidentified Data K-marginal Score": "400" + "Absolute Diff. From Deidentified Data K-marginal Score": "401" }, { "Sub-Sample Size": "90%", "Sub-Sample K-Marginal Score": 981, "Deidentified Data K-marginal score": 571, "Absolute Diff. From Deidentified Data K-marginal Score": "410" - }, - { - "Sub-Sample Size": "100%", - "Sub-Sample K-Marginal Score": 1000, - "Deidentified Data K-marginal score": 571, - "Absolute Diff. From Deidentified Data K-marginal Score": "429" } ], "type": "table", @@ -251,7 +285,7 @@ "Montgomery County (South)--Bethesda, Potomac & North Bethesda", "https://censusreporter.org/profiles/79500US2401004-montgomery-county-south-bethesda-potomac-north-bethesda-puma-md/" ], - "40% Target Subsample Baseline": 904, + "40% Target Subsample Baseline": 899, "Deidentified Data Score": 2 }, { @@ -260,7 +294,7 @@ "Alexandria City", "https://censusreporter.org/profiles/79500US5151255-alexandria-city-puma-va/" ], - "40% Target Subsample Baseline": 871, + "40% Target Subsample Baseline": 877, "Deidentified Data Score": 4 }, { @@ -269,7 +303,7 @@ "NYC-Bronx Community District 1 & 2--Hunts Point, Longwood & Melrose", "https://censusreporter.org/profiles/79500US3603710-nyc-bronx-community-district-1-2-hunts-point-longwood-melrose-puma-ny/" ], - "40% Target Subsample Baseline": 821, + "40% Target Subsample Baseline": 807, "Deidentified Data Score": 98 }, { @@ -278,7 +312,7 @@ "Birmingham City (West)", "https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/" ], - "40% Target Subsample Baseline": 809, + "40% Target Subsample Baseline": 801, "Deidentified Data Score": 108 }, { @@ -287,7 +321,7 @@ "West North Dakota--Minot City", "https://censusreporter.org/profiles/79500US3800100-west-north-dakota-minot-city-puma-nd/" ], - "40% Target Subsample Baseline": 890, + "40% Target Subsample Baseline": 888, "Deidentified Data Score": 159 }, { @@ -296,7 +330,7 @@ "Las Vegas City (Southeast)", "https://censusreporter.org/profiles/79500US3200405-las-vegas-city-southeast-puma-nv/" ], - "40% Target Subsample Baseline": 841, + "40% Target Subsample Baseline": 843, "Deidentified Data Score": 170 }, { @@ -305,7 +339,7 @@ "Cherokee, Sequoyah & Adair Counties", "https://censusreporter.org/profiles/79500US4000200-cherokee-sequoyah-adair-counties-puma-ok/" ], - "40% Target Subsample Baseline": 834, + "40% Target Subsample Baseline": 836, "Deidentified Data Score": 194 }, { @@ -314,7 +348,7 @@ "Chicago City (South)--Auburn Gresham, Roseland, Chatham, Avalon Park & Burnside", "https://censusreporter.org/profiles/79500US1703531-chicago-city-south-auburn-gresham-roseland-chatham-avalon-park-burnside-puma-il/" ], - "40% Target Subsample Baseline": 833, + "40% Target Subsample Baseline": 834, "Deidentified Data Score": 226 }, { @@ -341,7 +375,7 @@ "Atlanta Regional Commission--Fulton County (Central)--Atlanta City (Central)", "https://censusreporter.org/profiles/79500US1304600-atlanta-regional-commission-fulton-county-central-atlanta-city-central-puma-ga/" ], - "40% Target Subsample Baseline": 858, + "40% Target Subsample Baseline": 855, "Deidentified Data Score": 263 }, { @@ -350,7 +384,7 @@ "NYC-Brooklyn Community District 17--East Flatbush, Farragut & Rugby", "https://censusreporter.org/profiles/79500US3604010-nyc-brooklyn-community-district-17-east-flatbush-farragut-rugby-puma-ny/" ], - "40% Target Subsample Baseline": 837, + "40% Target Subsample Baseline": 843, "Deidentified Data Score": 277 }, { @@ -359,7 +393,7 @@ "Central Region--Jackson City (East & Central)", "https://censusreporter.org/profiles/79500US2801100-central-region-jackson-city-east-central-puma-ms/" ], - "40% Target Subsample Baseline": 853, + "40% Target Subsample Baseline": 850, "Deidentified Data Score": 281 }, { @@ -368,7 +402,7 @@ "Santa Clara County (Southwest)--Cupertino, Saratoga Cities & Los Gatos Town", "https://censusreporter.org/profiles/79500US0608507-santa-clara-county-southwest-cupertino-saratoga-cities-los-gatos-town-puma-ca/" ], - "40% Target Subsample Baseline": 879, + "40% Target Subsample Baseline": 886, "Deidentified Data Score": 321 }, { @@ -377,7 +411,7 @@ "St. Louis City (North)", "https://censusreporter.org/profiles/79500US2901901-st-louis-city-north-puma-mo/" ], - "40% Target Subsample Baseline": 843, + "40% Target Subsample Baseline": 838, "Deidentified Data Score": 321 }, { @@ -386,7 +420,7 @@ "Des Moines City", "https://censusreporter.org/profiles/79500US1901700-des-moines-city-puma-ia/" ], - "40% Target Subsample Baseline": 847, + "40% Target Subsample Baseline": 850, "Deidentified Data Score": 327 }, { @@ -395,7 +429,7 @@ "Washtenaw County (East Central)--Ann Arbor City Area", "https://censusreporter.org/profiles/79500US2602702-washtenaw-county-east-central-ann-arbor-city-area-puma-mi/" ], - "40% Target Subsample Baseline": 843, + "40% Target Subsample Baseline": 852, "Deidentified Data Score": 333 }, { @@ -404,7 +438,7 @@ "Arlington County (North)", "https://censusreporter.org/profiles/79500US5101301-arlington-county-north-puma-va/" ], - "40% Target Subsample Baseline": 864, + "40% Target Subsample Baseline": 869, "Deidentified Data Score": 371 }, { @@ -413,7 +447,7 @@ "San Francisco County (North & East)--North Beach & Chinatown", "https://censusreporter.org/profiles/79500US0607502-san-francisco-county-north-east-north-beach-chinatown-puma-ca/" ], - "40% Target Subsample Baseline": 857, + "40% Target Subsample Baseline": 859, "Deidentified Data Score": 413 }, { @@ -422,7 +456,7 @@ "Boulder County (Central)--Boulder City", "https://censusreporter.org/profiles/79500US0800803-boulder-county-central-boulder-city-puma-co/" ], - "40% Target Subsample Baseline": 866, + "40% Target Subsample Baseline": 868, "Deidentified Data Score": 425 } ], @@ -733,7 +767,7 @@ "-1": [ { "name": null, - "data": "This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you\u2019re using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes\u2013 the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.", + "data": "This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you're using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.", "type": "string", "dotted_break": false }, @@ -800,7 +834,7 @@ }, { "name": null, - "data": "The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP = \u201cN\u201d, individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. ", + "data": "The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP='N', individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. ", "type": "string", "dotted_break": false }, @@ -832,6 +866,12 @@ "metric_name": "Inconsistencies", "attachments": { "-1": [ + { + "name": null, + "data": "In real world tabular data, it's common for record features to have some deterministic, publicly known dependencies on each other: knowing someone's AGEP= 3 necessarily tells you something about their marital status, income and educational attainment. Different deidentification methods may be better or worse at automatically preserving these relationships. When they fail (ex: producing toddlers with PhDs) we say those records contain \"inconsistencies\". Our consistency check metric below is not exhaustive, we don't catch everything the way a production-grade system should, but this will give you a sense of how consistency is preserved for age, work and household features. Note that different deidentification approaches may do better or worse with different types of inconsistencies.", + "type": "string", + "dotted_break": false + }, { "name": "Summary", "data": { @@ -879,12 +919,12 @@ } }, { - "metric_name": "K-Marginal Score Breakdown", + "metric_name": "Worst Performing PUMAs Breakdown", "attachments": { "-1": [ { "name": null, - "data": "In the metrics above we\u2019ve considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score.", + "data": "In the metrics above we've considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score.", "type": "string", "dotted_break": false }, @@ -903,7 +943,7 @@ "Montgomery County (South)--Bethesda, Potomac & North Bethesda", "https://censusreporter.org/profiles/79500US2401004-montgomery-county-south-bethesda-potomac-north-bethesda-puma-md/" ], - "40% Target Subsample Baseline": 904, + "40% Target Subsample Baseline": 899, "Deidentified Data Score": 2 }, { @@ -912,7 +952,7 @@ "Alexandria City", "https://censusreporter.org/profiles/79500US5151255-alexandria-city-puma-va/" ], - "40% Target Subsample Baseline": 871, + "40% Target Subsample Baseline": 877, "Deidentified Data Score": 4 }, { @@ -921,7 +961,7 @@ "NYC-Bronx Community District 1 & 2--Hunts Point, Longwood & Melrose", "https://censusreporter.org/profiles/79500US3603710-nyc-bronx-community-district-1-2-hunts-point-longwood-melrose-puma-ny/" ], - "40% Target Subsample Baseline": 821, + "40% Target Subsample Baseline": 807, "Deidentified Data Score": 98 }, { @@ -930,7 +970,7 @@ "Birmingham City (West)", "https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/" ], - "40% Target Subsample Baseline": 809, + "40% Target Subsample Baseline": 801, "Deidentified Data Score": 108 }, { @@ -939,7 +979,7 @@ "West North Dakota--Minot City", "https://censusreporter.org/profiles/79500US3800100-west-north-dakota-minot-city-puma-nd/" ], - "40% Target Subsample Baseline": 890, + "40% Target Subsample Baseline": 888, "Deidentified Data Score": 159 } ], @@ -1064,19 +1104,37 @@ "-1": [ { "name": null, - "data": "This is a count of unique records in the target data that were exactly reproduced in the deidentified data. Because these records were unique outliers in the target data, and they still appear unchanged in the deidentified data, they are potentially vulnerable to reidentification.", + "data": "Unique Exact Match (UEM) is a simple privacy metric that counts the percentage of singleton records in the target that are also present in the deidentified data; these uniquely identifiable individuals leaked through the deidentification process.", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "Below we also include an estimate of the feature space size. The feature space is the set of all possible record values given the selected target data and feature subset. For instance, if we had two features, Hat [cap, bonnet] and Color [green, blue, purple], our feature space would consist of 2 x 3 = 6 possible combinations (e.g. 'green cap', 'blue bonnet'). Note that feature spaces are based on the feature set, not on what records actually exist in the data. Purple bonnet is a possible combination in this feature space, but it's likely no one in the hypothetical data owns a purple bonnet (and the count of that record value would be 0).", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "As we add features to the feature set, we increase the size of the feature space, but we don't change the actual number of records in the data-- it's the same people, but now they're spread out more thinly across a wider set of possible record values. Large feature spaces will disperse populations very sparsely (most possible record values will have count 0 or 1) and as a result the data will contain very many uniquely identifiable records. Intuitively, once you know enough pieces of information about someone, everyone becomes very distinct from everyone else. This can pose a challenge for privacy. ", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "The Target Data Properties below provides an estimate of the feature space size (100 is used for continuous feature), along with the portion of the records in the ground truth target data that are unique (ie, they are the only person with that record value, they have a count of 1). The Deidentified Data Properties reports the percentage of those uniquely identifiable individuals that are still present in the deidentified data. Because they are unique, real records, they are potentially vulnerable to reidentification.", "type": "string", "dotted_break": false }, { "name": "Target Data Properties", - "data": "Feature space size (possible combinations): -Highlight-64,449,000-Highlight-
Number of unique records in Target Data: -Highlight-23908 (87.73%-Highlight-)", + "data": "Feature space size (possible combinations): -Highlight-4.990e+11-Highlight-
Number of unique records in Target Data: -Highlight-23908 (87.73%-Highlight-)", "type": "string", "dotted_break": false }, { "name": "Deidentified Data Properties", - "data": "Number of unique Target Data records exactly matched in Deid. Data: -Highlight-208 (0.76%)-Highlight-", + "data": "Number of unique Target Data records exactly matched in Deid. Data: -Highlight-208 (0.87%)-Highlight-", "type": "string", "dotted_break": false } @@ -1101,7 +1159,7 @@ }, { "name": null, - "data": "PUMA, SEX, HISP, MSP, RAC1P, OWN_RENT", + "data": "SEX, MSP, RAC1P, OWN_RENT, HISP, PUMA", "type": "string", "dotted_break": false }, @@ -1717,6 +1775,12 @@ "type": "string", "dotted_break": false }, + { + "name": null, + "data": "There are a total of 271 possible codes for INDP, 269 of these codes appear in the Diverse Community Data Excerpts (233 in MA, 264 in Texas and National)", + "type": "string", + "dotted_break": false + }, { "name": "INDP_CAT: Industry categories", "data": [ diff --git a/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_05-19-2023T18.01.12/univariate/AGEP.jpg b/sdnist/report/sample-reports/report_pac_synth_e_10_family_focused_na2019_national2019_06-19-2023T20.33.47/univariate/AGEP.jpg similarity index 100% rename from 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f837d82..3ed9710 100644 --- a/sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_05-19-2023T18.01.12/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv +++ b/sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_national2019_06-19-2023T20.33.47/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv @@ -1,6 +1,6 @@ ,PUMA,40% Target Subsample Baseline,Deidentified Data Score -0,36-03710,833,374 -1,32-00405,864,426 -2,13-04600,864,469 +0,36-03710,826,374 +1,32-00405,866,426 +2,13-04600,866,469 3,24-01004,908,493 -4,17-03529,869,513 +4,17-03529,867,513 diff --git a/sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv b/sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv similarity index 82% rename from sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv rename to sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv index dc7e839..b0cd8a8 100644 --- a/sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv +++ b/sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv @@ -1,21 +1,21 @@ ,PUMA,40% Target Subsample Baseline,Deidentified Data Score -0,"['36-03710', 'NYC-Bronx Community District 1 & 2--Hunts Point, Longwood & Melrose', 'https://censusreporter.org/profiles/79500US3603710-nyc-bronx-community-district-1-2-hunts-point-longwood-melrose-puma-ny/']",833,374 -1,"['32-00405', 'Las Vegas City (Southeast)', 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Potomac & North Bethesda', 'https://censusreporter.org/profiles/79500US2401004-montgomery-county-south-bethesda-potomac-north-bethesda-puma-md/']",908,493 -4,"['17-03529', 'Chicago City (South)--South Shore, Hyde Park, Woodlawn, Grand Boulevard & Douglas', 'https://censusreporter.org/profiles/79500US1703529-chicago-city-south-south-shore-hyde-park-woodlawn-grand-boulevard-douglas-puma-il/']",869,513 +4,"['17-03529', 'Chicago City (South)--South Shore, Hyde Park, Woodlawn, Grand Boulevard & Douglas', 'https://censusreporter.org/profiles/79500US1703529-chicago-city-south-south-shore-hyde-park-woodlawn-grand-boulevard-douglas-puma-il/']",867,513 5,"['06-07502', 'San Francisco County (North & East)--North Beach & Chinatown', 'https://censusreporter.org/profiles/79500US0607502-san-francisco-county-north-east-north-beach-chinatown-puma-ca/']",855,521 -6,"['51-51255', 'Alexandria City', 'https://censusreporter.org/profiles/79500US5151255-alexandria-city-puma-va/']",877,527 -7,"['28-01100', 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a/sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_05-19-2023T18.01.12/report.html +++ b/sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_national2019_06-19-2023T20.33.47/report.html @@ -160,11 +160,11 @@

Data Evaluation Report


-

Report created on: May 19, 2023 18:15:36

+

Report created on: June 19, 2023 20:48:44

Created with - SDNIST v2.2.1 + SDNIST v2.3.0

@@ -230,7 +230,67 @@

- Library + Target Dataset + + + + national2019 + + + + + + + + + + + Epsilon + + + + 10 + + + + + + + + + + + Variant Label + + + + preprocessor-epsilon: 3 + + + + + + + + + + + Algorithm Type + + + + query matching + + + + + + + + + + + Library Name @@ -245,7 +305,7 @@

- Feature Set + Feature Set Name @@ -260,11 +320,11 @@

- Target Dataset + Privacy Category - national2019 + dp @@ -275,11 +335,11 @@

- Epsilon + Deid Data Id - 10 + b0a133c78c4b15eb14a47fc0267ab5926227648d @@ -290,11 +350,11 @@

- Variant Label + Features List - preprocessor-epsilon: 3 + PUMA, SEX, MSP, HISP, RAC1P, OWN_RENT, INDP_CAT, EDU, PINCP_DECILE @@ -305,11 +365,56 @@

- Privacy + Privacy Label Detail - Differential Privacy + The pac-synth synthesizer will suppress marginal combinations that could uniquely fingerprint individuals (similar to k-anonymity). pac-synth is a differentially-private synthesizer that computes differentially private marginals to build synthetic data. + + + + + + + + + + + Submission Timestamp + + + + 5/20/2023 00:00:00 + + + + + + + + + + + Team + + + + CRC + + + + + + + + + + + Research Papers + + + + https://github.com/microsoft/synthetic-data-showcase/tree/main/docs/dp @@ -867,7 +972,7 @@

- K-Marginal score of the deidentified data closely resembles K-Marginal score of a 10% sub-sample of the target data. + K-Marginal score of the deidentified data closely resembles K-Marginal score of a 5% sub-sample of the target data.
@@ -899,29 +1004,50 @@

- + Sub-Sample Size - + Sub-Sample K-Marginal Score - + Deidentified Data K-marginal score - + Absolute Diff. From Deidentified Data K-marginal Score + + + + + + + + + 1% + + + + 571 + + + + 776 + + + + 205 @@ -932,11 +1058,11 @@

- 10% + 5% - 847 + 784 @@ -944,7 +1070,7 @@

- 71 + 8 @@ -958,11 +1084,11 @@

- 20% + 10% - 898 + 848 @@ -970,7 +1096,7 @@

- 122 + 72 @@ -981,11 +1107,11 @@

- 30% + 20% - 919 + 898 @@ -993,7 +1119,7 @@

- 143 + 122 @@ -1004,11 +1130,11 @@

- 40% + 30% - 937 + 923 @@ -1016,7 +1142,7 @@

- 161 + 147 @@ -1027,11 +1153,11 @@

- 50% + 40% - 949 + 936 @@ -1039,7 +1165,7 @@

- 173 + 160 @@ -1050,11 +1176,11 @@

- 60% + 50% - 957 + 947 @@ -1062,7 +1188,7 @@

- 181 + 171 @@ -1073,11 +1199,11 @@

- 70% + 60% - 965 + 958 @@ -1085,7 +1211,7 @@

- 189 + 182 @@ -1096,11 +1222,11 @@

- 80% + 70% - 973 + 966 @@ -1108,7 +1234,7 @@

- 197 + 190 @@ -1119,11 +1245,11 @@

- 90% + 80% - 982 + 974 @@ -1131,7 +1257,7 @@

- 206 + 198 @@ -1142,11 +1268,11 @@

- 100% + 90% - 1000 + 982 @@ -1154,7 +1280,7 @@

- 224 + 206 @@ -1258,7 +1384,7 @@

- 833 + 826 @@ -1280,7 +1406,7 @@

- 864 + 866 @@ -1302,7 +1428,7 @@

- 864 + 866 @@ -1346,7 +1472,7 @@

- 869 + 867 @@ -1390,7 +1516,7 @@

- 877 + 874 @@ -1412,7 +1538,7 @@

- 862 + 860 @@ -1456,7 +1582,7 @@

- 836 + 816 @@ -1478,7 +1604,7 @@

- 846 + 852 @@ -1500,7 +1626,7 @@

- 897 + 891 @@ -1522,7 +1648,7 @@

- 858 + 847 @@ -1544,7 +1670,7 @@

- 868 + 856 @@ -1566,7 +1692,7 @@

- 873 + 866 @@ -1588,7 +1714,7 @@

- 898 + 897 @@ -1610,7 +1736,7 @@

- 859 + 856 @@ -1632,7 +1758,7 @@

- 895 + 897 @@ -1654,7 +1780,7 @@

- 870 + 865 @@ -1676,7 +1802,7 @@

- 874 + 872 @@ -3804,7 +3930,7 @@

- This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you’re using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes– the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive. + This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you're using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.
@@ -4087,7 +4213,7 @@

- The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP = “N”, individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. + The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP='N', individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape.
@@ -4197,6 +4323,34 @@

+
+ +
+ + + + +
+ In real world tabular data, it's common for record features to have some deterministic, publicly known dependencies on each other: knowing someone's AGEP= 3 necessarily tells you something about their marital status, income and educational attainment. Different deidentification methods may be better or worse at automatically preserving these relationships. When they fail (ex: producing toddlers with PhDs) we say those records contain "inconsistencies". Our consistency check metric below is not exhaustive, we don't catch everything the way a production-grade system should, but this will give you a sense of how consistency is preserved for age, work and household features. Note that different deidentification approaches may do better or worse with different types of inconsistencies. +
+ + + +
+ + + + + + + + + + + + + +

@@ -4775,7 +4929,7 @@

- K-Marginal Score Breakdown: + Worst Performing PUMAs Breakdown:

@@ -4799,7 +4953,7 @@

- In the metrics above we’ve considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score. + In the metrics above we've considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score.
@@ -4898,7 +5052,7 @@

- 833 + 826 @@ -4920,7 +5074,7 @@

- 864 + 866 @@ -4942,7 +5096,7 @@

- 864 + 866 @@ -4986,7 +5140,7 @@

- 869 + 867 @@ -5438,6 +5592,7 @@

Privacy Evaluation

+

@@ -5465,7 +5620,91 @@

- This is a count of unique records in the target data that were exactly reproduced in the deidentified data. Because these records were unique outliers in the target data, and they still appear unchanged in the deidentified data, they are potentially vulnerable to reidentification. + Unique Exact Match (UEM) is a simple privacy metric that counts the percentage of singleton records in the target that are also present in the deidentified data; these uniquely identifiable individuals leaked through the deidentification process. +
+ + + +

+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ Below we also include an estimate of the feature space size. The feature space is the set of all possible record values given the selected target data and feature subset. For instance, if we had two features, Hat [cap, bonnet] and Color [green, blue, purple], our feature space would consist of 2 x 3 = 6 possible combinations (e.g. 'green cap', 'blue bonnet'). Note that feature spaces are based on the feature set, not on what records actually exist in the data. Purple bonnet is a possible combination in this feature space, but it's likely no one in the hypothetical data owns a purple bonnet (and the count of that record value would be 0). +
+ + + +
+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ As we add features to the feature set, we increase the size of the feature space, but we don't change the actual number of records in the data-- it's the same people, but now they're spread out more thinly across a wider set of possible record values. Large feature spaces will disperse populations very sparsely (most possible record values will have count 0 or 1) and as a result the data will contain very many uniquely identifiable records. Intuitively, once you know enough pieces of information about someone, everyone becomes very distinct from everyone else. This can pose a challenge for privacy. +
+ + + +
+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ The Target Data Properties below provides an estimate of the feature space size (100 is used for continuous feature), along with the portion of the records in the ground truth target data that are unique (ie, they are the only person with that record value, they have a count of 1). The Deidentified Data Properties reports the percentage of those uniquely identifiable individuals that are still present in the deidentified data. Because they are unique, real records, they are potentially vulnerable to reidentification.
@@ -5509,7 +5748,7 @@

- 167,567,400 + 1.081e+08 @@ -5571,7 +5810,7 @@

- 1579 (5.79%) + 1579 (9.79%) @@ -5598,6 +5837,12 @@

+
+
+
+
+ +

@@ -5688,7 +5933,7 @@

- PUMA, SEX, INDP_CAT, HISP, MSP, RAC1P, OWN_RENT, EDU + HISP, EDU, MSP, INDP_CAT, SEX, OWN_RENT, RAC1P, PUMA
@@ -8562,6 +8807,34 @@

+
+ +
+ + + + +
+ There are a total of 271 possible codes for INDP, 269 of these codes appear in the Diverse Community Data Excerpts (233 in MA, 264 in Texas and National) +
+ + + +
+ + + + + + + + + + + + + +

diff --git a/sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_05-19-2023T18.01.12/ui.json b/sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_national2019_06-19-2023T20.33.47/ui.json similarity index 93% rename from sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_05-19-2023T18.01.12/ui.json rename to sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_national2019_06-19-2023T20.33.47/ui.json index 5b9fd27..cc5ced9 100644 --- a/sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_05-19-2023T18.01.12/ui.json +++ b/sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_national2019_06-19-2023T20.33.47/ui.json @@ -1,6 +1,6 @@ { - "Created on": "May 19, 2023 18:15:36", - "version": "2.2.1", + "Created on": "June 19, 2023 20:48:44", + "version": "2.3.0", "data_description": { "target": [ { @@ -42,14 +42,6 @@ "Label Name": "Algorithm Name", "Label Value": "pacsynth" }, - { - "Label Name": "Library", - "Label Value": "smartnoise-synth" - }, - { - "Label Name": "Feature Set", - "Label Value": "industry-focused" - }, { "Label Name": "Target Dataset", "Label Value": "national2019" @@ -63,8 +55,44 @@ "Label Value": "preprocessor-epsilon: 3" }, { - "Label Name": "Privacy", - "Label Value": "Differential Privacy" + "Label Name": "Algorithm Type", + "Label Value": "query matching" + }, + { + "Label Name": "Library Name", + "Label Value": "smartnoise-synth" + }, + { + "Label Name": "Feature Set Name", + "Label Value": "industry-focused" + }, + { + "Label Name": "Privacy Category", + "Label Value": "dp" + }, + { + "Label Name": "Deid Data Id", + "Label Value": "b0a133c78c4b15eb14a47fc0267ab5926227648d" + }, + { + "Label Name": "Features List", + "Label Value": "PUMA, SEX, MSP, HISP, RAC1P, OWN_RENT, INDP_CAT, EDU, PINCP_DECILE" + }, + { + "Label Name": "Privacy Label Detail", + "Label Value": "The pac-synth synthesizer will suppress marginal combinations that could uniquely fingerprint individuals (similar to k-anonymity). pac-synth is a differentially-private synthesizer that computes differentially private marginals to build synthetic data. " + }, + { + "Label Name": "Submission Timestamp", + "Label Value": "5/20/2023 00:00:00" + }, + { + "Label Name": "Team", + "Label Value": "CRC" + }, + { + "Label Name": "Research Papers", + "Label Value": "https://github.com/microsoft/synthetic-data-showcase/tree/main/docs/dp" } ], "validations": [] @@ -152,7 +180,7 @@ }, { "name": null, - "data": "K-Marginal score of the deidentified data closely resembles K-Marginal score of a 10% sub-sample of the target data.", + "data": "K-Marginal score of the deidentified data closely resembles K-Marginal score of a 5% sub-sample of the target data.", "type": "string", "dotted_break": false }, @@ -160,12 +188,24 @@ "name": null, "data": [ { - "Sub-Sample Size": "10%", - "Sub-Sample K-Marginal Score": 847, + "Sub-Sample Size": "1%", + "Sub-Sample K-Marginal Score": 571, + "Deidentified Data K-marginal score": 776, + "Absolute Diff. From Deidentified Data K-marginal Score": "205" + }, + { + "Sub-Sample Size": "5%", + "Sub-Sample K-Marginal Score": 784, "Deidentified Data K-marginal score": 776, - "Absolute Diff. From Deidentified Data K-marginal Score": "71", + "Absolute Diff. From Deidentified Data K-marginal Score": "8", "min_idx": true }, + { + "Sub-Sample Size": "10%", + "Sub-Sample K-Marginal Score": 848, + "Deidentified Data K-marginal score": 776, + "Absolute Diff. From Deidentified Data K-marginal Score": "72" + }, { "Sub-Sample Size": "20%", "Sub-Sample K-Marginal Score": 898, @@ -174,51 +214,45 @@ }, { "Sub-Sample Size": "30%", - "Sub-Sample K-Marginal Score": 919, + "Sub-Sample K-Marginal Score": 923, "Deidentified Data K-marginal score": 776, - "Absolute Diff. From Deidentified Data K-marginal Score": "143" + "Absolute Diff. From Deidentified Data K-marginal Score": "147" }, { "Sub-Sample Size": "40%", - "Sub-Sample K-Marginal Score": 937, + "Sub-Sample K-Marginal Score": 936, "Deidentified Data K-marginal score": 776, - "Absolute Diff. From Deidentified Data K-marginal Score": "161" + "Absolute Diff. From Deidentified Data K-marginal Score": "160" }, { "Sub-Sample Size": "50%", - "Sub-Sample K-Marginal Score": 949, + "Sub-Sample K-Marginal Score": 947, "Deidentified Data K-marginal score": 776, - "Absolute Diff. From Deidentified Data K-marginal Score": "173" + "Absolute Diff. From Deidentified Data K-marginal Score": "171" }, { "Sub-Sample Size": "60%", - "Sub-Sample K-Marginal Score": 957, + "Sub-Sample K-Marginal Score": 958, "Deidentified Data K-marginal score": 776, - "Absolute Diff. From Deidentified Data K-marginal Score": "181" + "Absolute Diff. From Deidentified Data K-marginal Score": "182" }, { "Sub-Sample Size": "70%", - "Sub-Sample K-Marginal Score": 965, + "Sub-Sample K-Marginal Score": 966, "Deidentified Data K-marginal score": 776, - "Absolute Diff. From Deidentified Data K-marginal Score": "189" + "Absolute Diff. From Deidentified Data K-marginal Score": "190" }, { "Sub-Sample Size": "80%", - "Sub-Sample K-Marginal Score": 973, + "Sub-Sample K-Marginal Score": 974, "Deidentified Data K-marginal score": 776, - "Absolute Diff. From Deidentified Data K-marginal Score": "197" + "Absolute Diff. From Deidentified Data K-marginal Score": "198" }, { "Sub-Sample Size": "90%", "Sub-Sample K-Marginal Score": 982, "Deidentified Data K-marginal score": 776, "Absolute Diff. From Deidentified Data K-marginal Score": "206" - }, - { - "Sub-Sample Size": "100%", - "Sub-Sample K-Marginal Score": 1000, - "Deidentified Data K-marginal score": 776, - "Absolute Diff. From Deidentified Data K-marginal Score": "224" } ], "type": "table", @@ -239,7 +273,7 @@ "NYC-Bronx Community District 1 & 2--Hunts Point, Longwood & Melrose", "https://censusreporter.org/profiles/79500US3603710-nyc-bronx-community-district-1-2-hunts-point-longwood-melrose-puma-ny/" ], - "40% Target Subsample Baseline": 833, + "40% Target Subsample Baseline": 826, "Deidentified Data Score": 374 }, { @@ -248,7 +282,7 @@ "Las Vegas City (Southeast)", "https://censusreporter.org/profiles/79500US3200405-las-vegas-city-southeast-puma-nv/" ], - "40% Target Subsample Baseline": 864, + "40% Target Subsample Baseline": 866, "Deidentified Data Score": 426 }, { @@ -257,7 +291,7 @@ "Atlanta Regional Commission--Fulton County (Central)--Atlanta City (Central)", "https://censusreporter.org/profiles/79500US1304600-atlanta-regional-commission-fulton-county-central-atlanta-city-central-puma-ga/" ], - "40% Target Subsample Baseline": 864, + "40% Target Subsample Baseline": 866, "Deidentified Data Score": 469 }, { @@ -275,7 +309,7 @@ "Chicago City (South)--South Shore, Hyde Park, Woodlawn, Grand Boulevard & Douglas", "https://censusreporter.org/profiles/79500US1703529-chicago-city-south-south-shore-hyde-park-woodlawn-grand-boulevard-douglas-puma-il/" ], - "40% Target Subsample Baseline": 869, + "40% Target Subsample Baseline": 867, "Deidentified Data Score": 513 }, { @@ -293,7 +327,7 @@ "Alexandria City", "https://censusreporter.org/profiles/79500US5151255-alexandria-city-puma-va/" ], - "40% Target Subsample Baseline": 877, + "40% Target Subsample Baseline": 874, "Deidentified Data Score": 527 }, { @@ -302,7 +336,7 @@ "Central Region--Jackson City (East & Central)", "https://censusreporter.org/profiles/79500US2801100-central-region-jackson-city-east-central-puma-ms/" ], - "40% Target Subsample Baseline": 862, + "40% Target Subsample Baseline": 860, "Deidentified Data Score": 536 }, { @@ -320,7 +354,7 @@ "Birmingham City (West)", "https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/" ], - "40% Target Subsample Baseline": 836, + "40% Target Subsample Baseline": 816, "Deidentified Data Score": 545 }, { @@ -329,7 +363,7 @@ "St. Louis City (North)", "https://censusreporter.org/profiles/79500US2901901-st-louis-city-north-puma-mo/" ], - "40% Target Subsample Baseline": 846, + "40% Target Subsample Baseline": 852, "Deidentified Data Score": 548 }, { @@ -338,7 +372,7 @@ "Santa Clara County (Southwest)--Cupertino, Saratoga Cities & Los Gatos Town", "https://censusreporter.org/profiles/79500US0608507-santa-clara-county-southwest-cupertino-saratoga-cities-los-gatos-town-puma-ca/" ], - "40% Target Subsample Baseline": 897, + "40% Target Subsample Baseline": 891, "Deidentified Data Score": 560 }, { @@ -347,7 +381,7 @@ "Chicago City (South)--Auburn Gresham, Roseland, Chatham, Avalon Park & Burnside", "https://censusreporter.org/profiles/79500US1703531-chicago-city-south-auburn-gresham-roseland-chatham-avalon-park-burnside-puma-il/" ], - "40% Target Subsample Baseline": 858, + "40% Target Subsample Baseline": 847, "Deidentified Data Score": 584 }, { @@ -356,7 +390,7 @@ "Cherokee, Sequoyah & Adair Counties", "https://censusreporter.org/profiles/79500US4000200-cherokee-sequoyah-adair-counties-puma-ok/" ], - "40% Target Subsample Baseline": 868, + "40% Target Subsample Baseline": 856, "Deidentified Data Score": 592 }, { @@ -365,7 +399,7 @@ "Arlington County (North)", "https://censusreporter.org/profiles/79500US5101301-arlington-county-north-puma-va/" ], - "40% Target Subsample Baseline": 873, + "40% Target Subsample Baseline": 866, "Deidentified Data Score": 616 }, { @@ -374,7 +408,7 @@ "East Montana (Outside Billings City)", "https://censusreporter.org/profiles/79500US3000600-east-montana-outside-billings-city-puma-mt/" ], - "40% Target Subsample Baseline": 898, + "40% Target Subsample Baseline": 897, "Deidentified Data Score": 617 }, { @@ -383,7 +417,7 @@ "Washtenaw County (East Central)--Ann Arbor City Area", "https://censusreporter.org/profiles/79500US2602702-washtenaw-county-east-central-ann-arbor-city-area-puma-mi/" ], - "40% Target Subsample Baseline": 859, + "40% Target Subsample Baseline": 856, "Deidentified Data Score": 619 }, { @@ -392,7 +426,7 @@ "West North Dakota--Minot City", "https://censusreporter.org/profiles/79500US3800100-west-north-dakota-minot-city-puma-nd/" ], - "40% Target Subsample Baseline": 895, + "40% Target Subsample Baseline": 897, "Deidentified Data Score": 627 }, { @@ -401,7 +435,7 @@ "Des Moines City", "https://censusreporter.org/profiles/79500US1901700-des-moines-city-puma-ia/" ], - "40% Target Subsample Baseline": 870, + "40% Target Subsample Baseline": 865, "Deidentified Data Score": 627 }, { @@ -410,7 +444,7 @@ "Boulder County (Central)--Boulder City", "https://censusreporter.org/profiles/79500US0800803-boulder-county-central-boulder-city-puma-co/" ], - "40% Target Subsample Baseline": 874, + "40% Target Subsample Baseline": 872, "Deidentified Data Score": 638 } ], @@ -1063,7 +1097,7 @@ "-1": [ { "name": null, - "data": "This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you\u2019re using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes\u2013 the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.", + "data": "This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you're using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.", "type": "string", "dotted_break": false }, @@ -1130,7 +1164,7 @@ }, { "name": null, - "data": "The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP = \u201cN\u201d, individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. ", + "data": "The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP='N', individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. ", "type": "string", "dotted_break": false }, @@ -1162,6 +1196,12 @@ "metric_name": "Inconsistencies", "attachments": { "-1": [ + { + "name": null, + "data": "In real world tabular data, it's common for record features to have some deterministic, publicly known dependencies on each other: knowing someone's AGEP= 3 necessarily tells you something about their marital status, income and educational attainment. Different deidentification methods may be better or worse at automatically preserving these relationships. When they fail (ex: producing toddlers with PhDs) we say those records contain \"inconsistencies\". Our consistency check metric below is not exhaustive, we don't catch everything the way a production-grade system should, but this will give you a sense of how consistency is preserved for age, work and household features. Note that different deidentification approaches may do better or worse with different types of inconsistencies.", + "type": "string", + "dotted_break": false + }, { "name": "Summary", "data": { @@ -1255,12 +1295,12 @@ } }, { - "metric_name": "K-Marginal Score Breakdown", + "metric_name": "Worst Performing PUMAs Breakdown", "attachments": { "-1": [ { "name": null, - "data": "In the metrics above we\u2019ve considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score.", + "data": "In the metrics above we've considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score.", "type": "string", "dotted_break": false }, @@ -1279,7 +1319,7 @@ "NYC-Bronx Community District 1 & 2--Hunts Point, Longwood & Melrose", "https://censusreporter.org/profiles/79500US3603710-nyc-bronx-community-district-1-2-hunts-point-longwood-melrose-puma-ny/" ], - "40% Target Subsample Baseline": 833, + "40% Target Subsample Baseline": 826, "Deidentified Data Score": 374 }, { @@ -1288,7 +1328,7 @@ "Las Vegas City (Southeast)", "https://censusreporter.org/profiles/79500US3200405-las-vegas-city-southeast-puma-nv/" ], - "40% Target Subsample Baseline": 864, + "40% Target Subsample Baseline": 866, "Deidentified Data Score": 426 }, { @@ -1297,7 +1337,7 @@ "Atlanta Regional Commission--Fulton County (Central)--Atlanta City (Central)", "https://censusreporter.org/profiles/79500US1304600-atlanta-regional-commission-fulton-county-central-atlanta-city-central-puma-ga/" ], - "40% Target Subsample Baseline": 864, + "40% Target Subsample Baseline": 866, "Deidentified Data Score": 469 }, { @@ -1315,7 +1355,7 @@ "Chicago City (South)--South Shore, Hyde Park, Woodlawn, Grand Boulevard & Douglas", "https://censusreporter.org/profiles/79500US1703529-chicago-city-south-south-shore-hyde-park-woodlawn-grand-boulevard-douglas-puma-il/" ], - "40% Target Subsample Baseline": 869, + "40% Target Subsample Baseline": 867, "Deidentified Data Score": 513 } ], @@ -1434,19 +1474,37 @@ "-1": [ { "name": null, - "data": "This is a count of unique records in the target data that were exactly reproduced in the deidentified data. Because these records were unique outliers in the target data, and they still appear unchanged in the deidentified data, they are potentially vulnerable to reidentification.", + "data": "Unique Exact Match (UEM) is a simple privacy metric that counts the percentage of singleton records in the target that are also present in the deidentified data; these uniquely identifiable individuals leaked through the deidentification process.", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "Below we also include an estimate of the feature space size. The feature space is the set of all possible record values given the selected target data and feature subset. For instance, if we had two features, Hat [cap, bonnet] and Color [green, blue, purple], our feature space would consist of 2 x 3 = 6 possible combinations (e.g. 'green cap', 'blue bonnet'). Note that feature spaces are based on the feature set, not on what records actually exist in the data. Purple bonnet is a possible combination in this feature space, but it's likely no one in the hypothetical data owns a purple bonnet (and the count of that record value would be 0).", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "As we add features to the feature set, we increase the size of the feature space, but we don't change the actual number of records in the data-- it's the same people, but now they're spread out more thinly across a wider set of possible record values. Large feature spaces will disperse populations very sparsely (most possible record values will have count 0 or 1) and as a result the data will contain very many uniquely identifiable records. Intuitively, once you know enough pieces of information about someone, everyone becomes very distinct from everyone else. This can pose a challenge for privacy. ", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "The Target Data Properties below provides an estimate of the feature space size (100 is used for continuous feature), along with the portion of the records in the ground truth target data that are unique (ie, they are the only person with that record value, they have a count of 1). The Deidentified Data Properties reports the percentage of those uniquely identifiable individuals that are still present in the deidentified data. Because they are unique, real records, they are potentially vulnerable to reidentification.", "type": "string", "dotted_break": false }, { "name": "Target Data Properties", - "data": "Feature space size (possible combinations): -Highlight-167,567,400-Highlight-
Number of unique records in Target Data: -Highlight-16132 (59.19%-Highlight-)", + "data": "Feature space size (possible combinations): -Highlight-1.081e+08-Highlight-
Number of unique records in Target Data: -Highlight-16132 (59.19%-Highlight-)", "type": "string", "dotted_break": false }, { "name": "Deidentified Data Properties", - "data": "Number of unique Target Data records exactly matched in Deid. Data: -Highlight-1579 (5.79%)-Highlight-", + "data": "Number of unique Target Data records exactly matched in Deid. Data: -Highlight-1579 (9.79%)-Highlight-", "type": "string", "dotted_break": false } @@ -1471,7 +1529,7 @@ }, { "name": null, - "data": "PUMA, SEX, INDP_CAT, HISP, MSP, RAC1P, OWN_RENT, EDU", + "data": "HISP, EDU, MSP, INDP_CAT, SEX, OWN_RENT, RAC1P, PUMA", "type": "string", "dotted_break": false }, @@ -2087,6 +2145,12 @@ "type": "string", "dotted_break": false }, + { + "name": null, + "data": "There are a total of 271 possible codes for INDP, 269 of these codes appear in the Diverse Community Data Excerpts (233 in MA, 264 in Texas and National)", + "type": "string", + "dotted_break": false + }, { "name": "INDP_CAT: Industry categories", "data": [ diff --git a/sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_05-19-2023T18.01.12/univariate/EDU.jpg b/sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_national2019_06-19-2023T20.33.47/univariate/EDU.jpg similarity index 100% rename from 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diff --git a/sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_05-19-2023T18.01.12/univariate/SEX.jpg b/sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_national2019_06-19-2023T20.33.47/univariate/SEX.jpg similarity index 100% rename from sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_05-19-2023T18.01.12/univariate/SEX.jpg rename to sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_national2019_06-19-2023T20.33.47/univariate/SEX.jpg diff --git a/sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_05-19-2023T18.01.12/univariate/SEX_counts.csv b/sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_national2019_06-19-2023T20.33.47/univariate/SEX_counts.csv similarity index 100% rename from sdnist/report/sample-reports/report_pac_synth_e_10_industry_focused_na2019_05-19-2023T18.01.12/univariate/SEX_counts.csv rename to 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b/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_05-19-2023T18.01.12/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv deleted file mode 100644 index cc82d95..0000000 --- a/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_05-19-2023T18.01.12/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv +++ /dev/null @@ -1,6 +0,0 @@ -,PUMA,40% Target Subsample Baseline,Deidentified Data Score -0,06-07502,853,123 -1,36-03710,831,192 -2,30-00600,880,212 -3,36-04010,850,231 -4,01-01301,821,239 diff --git a/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_05-19-2023T18.01.12/k_marginal_synopsys/subsample_error_comparison.csv b/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_05-19-2023T18.01.12/k_marginal_synopsys/subsample_error_comparison.csv deleted file mode 100644 index 528f60d..0000000 --- a/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_05-19-2023T18.01.12/k_marginal_synopsys/subsample_error_comparison.csv +++ /dev/null @@ -1,11 +0,0 @@ -,Sub-Sample Size,Sub-Sample K-Marginal Score,Deidentified Data K-marginal score,Absolute Diff. From Deidentified Data K-marginal Score -0,10%,843,683,160 -1,20%,892,683,209 -2,30%,917,683,234 -3,40%,932,683,249 -4,50%,946,683,263 -5,60%,954,683,271 -6,70%,964,683,281 -7,80%,972,683,289 -8,90%,981,683,298 -9,100%,1000,683,317 diff --git a/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_05-19-2023T18.01.12/pca/components_eigenvector.csv b/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_05-19-2023T18.01.12/pca/components_eigenvector.csv deleted file mode 100644 index 3ac5ec9..0000000 --- a/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_05-19-2023T18.01.12/pca/components_eigenvector.csv +++ /dev/null @@ -1,6 +0,0 @@ -,AGEP,DEAR,DENSITY,DEYE,DPHY,DREM,DVET,EDU,HISP,HOUSING_TYPE,INDP,INDP_CAT,MSP,NOC,NPF,OWN_RENT,PINCP,PINCP_DECILE,POVPIP,PUMA,PWGTP,RAC1P,SEX,WGTP 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index 0000000..38f9644 --- /dev/null +++ b/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_national2019_06-19-2023T20.33.47/k_marginal_breakdown/worst_5_puma_k_marginal_scores.csv @@ -0,0 +1,6 @@ +,PUMA,40% Target Subsample Baseline,Deidentified Data Score +0,06-07502,856,123 +1,36-03710,838,192 +2,30-00600,888,212 +3,36-04010,853,231 +4,01-01301,814,239 diff --git a/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv b/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv similarity index 82% rename from sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv rename to sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv index 29cd452..8f75d99 100644 --- a/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_05-19-2023T18.01.12/k_marginal_synopsys/score_in_each_puma.csv +++ b/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_national2019_06-19-2023T20.33.47/k_marginal_synopsys/score_in_each_puma.csv @@ -1,21 +1,21 @@ ,PUMA,40% Target Subsample Baseline,Deidentified Data Score -0,"['06-07502', 'San Francisco County (North & East)--North Beach & Chinatown', 'https://censusreporter.org/profiles/79500US0607502-san-francisco-county-north-east-north-beach-chinatown-puma-ca/']",853,123 -1,"['36-03710', 'NYC-Bronx Community District 1 & 2--Hunts Point, Longwood & Melrose', 'https://censusreporter.org/profiles/79500US3603710-nyc-bronx-community-district-1-2-hunts-point-longwood-melrose-puma-ny/']",831,192 -2,"['30-00600', 'East Montana (Outside Billings City)', 'https://censusreporter.org/profiles/79500US3000600-east-montana-outside-billings-city-puma-mt/']",880,212 -3,"['36-04010', 'NYC-Brooklyn Community District 17--East Flatbush, Farragut & Rugby', 'https://censusreporter.org/profiles/79500US3604010-nyc-brooklyn-community-district-17-east-flatbush-farragut-rugby-puma-ny/']",850,231 -4,"['01-01301', 'Birmingham City (West)', 'https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/']",821,239 -5,"['13-04600', 'Atlanta Regional Commission--Fulton County (Central)--Atlanta City (Central)', 'https://censusreporter.org/profiles/79500US1304600-atlanta-regional-commission-fulton-county-central-atlanta-city-central-puma-ga/']",854,283 -6,"['32-00405', 'Las Vegas City (Southeast)', 'https://censusreporter.org/profiles/79500US3200405-las-vegas-city-southeast-puma-nv/']",855,303 -7,"['29-01901', 'St. Louis City (North)', 'https://censusreporter.org/profiles/79500US2901901-st-louis-city-north-puma-mo/']",841,326 -8,"['08-00803', 'Boulder County (Central)--Boulder City', 'https://censusreporter.org/profiles/79500US0800803-boulder-county-central-boulder-city-puma-co/']",867,358 -9,"['51-51255', 'Alexandria City', 'https://censusreporter.org/profiles/79500US5151255-alexandria-city-puma-va/']",872,359 -10,"['17-03531', 'Chicago City (South)--Auburn Gresham, Roseland, Chatham, Avalon Park & Burnside', 'https://censusreporter.org/profiles/79500US1703531-chicago-city-south-auburn-gresham-roseland-chatham-avalon-park-burnside-puma-il/']",839,404 -11,"['40-00200', 'Cherokee, Sequoyah & Adair Counties', 'https://censusreporter.org/profiles/79500US4000200-cherokee-sequoyah-adair-counties-puma-ok/']",849,432 -12,"['24-01004', 'Montgomery County (South)--Bethesda, Potomac & North Bethesda', 'https://censusreporter.org/profiles/79500US2401004-montgomery-county-south-bethesda-potomac-north-bethesda-puma-md/']",900,434 -13,"['28-01100', 'Central Region--Jackson City (East & Central)', 'https://censusreporter.org/profiles/79500US2801100-central-region-jackson-city-east-central-puma-ms/']",853,440 +0,"['06-07502', 'San Francisco County (North & East)--North Beach & Chinatown', 'https://censusreporter.org/profiles/79500US0607502-san-francisco-county-north-east-north-beach-chinatown-puma-ca/']",856,123 +1,"['36-03710', 'NYC-Bronx Community District 1 & 2--Hunts Point, Longwood & Melrose', 'https://censusreporter.org/profiles/79500US3603710-nyc-bronx-community-district-1-2-hunts-point-longwood-melrose-puma-ny/']",838,192 +2,"['30-00600', 'East Montana (Outside Billings City)', 'https://censusreporter.org/profiles/79500US3000600-east-montana-outside-billings-city-puma-mt/']",888,212 +3,"['36-04010', 'NYC-Brooklyn Community District 17--East Flatbush, Farragut & Rugby', 'https://censusreporter.org/profiles/79500US3604010-nyc-brooklyn-community-district-17-east-flatbush-farragut-rugby-puma-ny/']",853,231 +4,"['01-01301', 'Birmingham City (West)', 'https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/']",814,239 +5,"['13-04600', 'Atlanta Regional Commission--Fulton County (Central)--Atlanta City (Central)', 'https://censusreporter.org/profiles/79500US1304600-atlanta-regional-commission-fulton-county-central-atlanta-city-central-puma-ga/']",864,283 +6,"['32-00405', 'Las Vegas City (Southeast)', 'https://censusreporter.org/profiles/79500US3200405-las-vegas-city-southeast-puma-nv/']",856,303 +7,"['29-01901', 'St. Louis City (North)', 'https://censusreporter.org/profiles/79500US2901901-st-louis-city-north-puma-mo/']",840,326 +8,"['08-00803', 'Boulder County (Central)--Boulder City', 'https://censusreporter.org/profiles/79500US0800803-boulder-county-central-boulder-city-puma-co/']",870,358 +9,"['51-51255', 'Alexandria City', 'https://censusreporter.org/profiles/79500US5151255-alexandria-city-puma-va/']",875,359 +10,"['17-03531', 'Chicago City (South)--Auburn Gresham, Roseland, Chatham, Avalon Park & Burnside', 'https://censusreporter.org/profiles/79500US1703531-chicago-city-south-auburn-gresham-roseland-chatham-avalon-park-burnside-puma-il/']",837,404 +11,"['40-00200', 'Cherokee, Sequoyah & Adair Counties', 'https://censusreporter.org/profiles/79500US4000200-cherokee-sequoyah-adair-counties-puma-ok/']",855,432 +12,"['24-01004', 'Montgomery County (South)--Bethesda, Potomac & North Bethesda', 'https://censusreporter.org/profiles/79500US2401004-montgomery-county-south-bethesda-potomac-north-bethesda-puma-md/']",901,434 +13,"['28-01100', 'Central Region--Jackson City (East & Central)', 'https://censusreporter.org/profiles/79500US2801100-central-region-jackson-city-east-central-puma-ms/']",852,440 14,"['06-08507', 'Santa Clara County (Southwest)--Cupertino, Saratoga Cities & Los Gatos Town', 'https://censusreporter.org/profiles/79500US0608507-santa-clara-county-southwest-cupertino-saratoga-cities-los-gatos-town-puma-ca/']",888,452 -15,"['26-02702', 'Washtenaw County (East Central)--Ann Arbor City Area', 'https://censusreporter.org/profiles/79500US2602702-washtenaw-county-east-central-ann-arbor-city-area-puma-mi/']",860,487 +15,"['26-02702', 'Washtenaw County (East Central)--Ann Arbor City Area', 'https://censusreporter.org/profiles/79500US2602702-washtenaw-county-east-central-ann-arbor-city-area-puma-mi/']",859,487 16,"['51-01301', 'Arlington County (North)', 'https://censusreporter.org/profiles/79500US5101301-arlington-county-north-puma-va/']",867,488 -17,"['17-03529', 'Chicago City (South)--South Shore, Hyde Park, Woodlawn, Grand Boulevard & Douglas', 'https://censusreporter.org/profiles/79500US1703529-chicago-city-south-south-shore-hyde-park-woodlawn-grand-boulevard-douglas-puma-il/']",870,491 -18,"['19-01700', 'Des Moines City', 'https://censusreporter.org/profiles/79500US1901700-des-moines-city-puma-ia/']",860,516 -19,"['38-00100', 'West North Dakota--Minot City', 'https://censusreporter.org/profiles/79500US3800100-west-north-dakota-minot-city-puma-nd/']",891,547 +17,"['17-03529', 'Chicago City (South)--South Shore, Hyde Park, Woodlawn, Grand Boulevard & Douglas', 'https://censusreporter.org/profiles/79500US1703529-chicago-city-south-south-shore-hyde-park-woodlawn-grand-boulevard-douglas-puma-il/']",877,491 +18,"['19-01700', 'Des Moines City', 'https://censusreporter.org/profiles/79500US1901700-des-moines-city-puma-ia/']",857,516 +19,"['38-00100', 'West North Dakota--Minot City', 'https://censusreporter.org/profiles/79500US3800100-west-north-dakota-minot-city-puma-nd/']",893,547 diff --git a/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_national2019_06-19-2023T20.33.47/k_marginal_synopsys/subsample_error_comparison.csv b/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_national2019_06-19-2023T20.33.47/k_marginal_synopsys/subsample_error_comparison.csv new file mode 100644 index 0000000..e44bdf1 --- 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a/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_05-19-2023T18.01.12/pearson_correlation/correlation_difference.csv b/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_national2019_06-19-2023T20.33.47/pearson_correlation/correlation_difference.csv similarity index 100% rename from sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_05-19-2023T18.01.12/pearson_correlation/correlation_difference.csv rename to sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_national2019_06-19-2023T20.33.47/pearson_correlation/correlation_difference.csv diff --git a/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_05-19-2023T18.01.12/pearson_correlation/pearson_corr_diff.jpg b/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_national2019_06-19-2023T20.33.47/pearson_correlation/pearson_corr_diff.jpg similarity index 100% rename from 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sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_national2019_06-19-2023T20.33.47/report.html index 67ce68a..aea37d2 100644 --- a/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_05-19-2023T18.01.12/report.html +++ b/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_national2019_06-19-2023T20.33.47/report.html @@ -160,11 +160,11 @@

Data Evaluation Report


-

Report created on: May 19, 2023 18:04:40

+

Report created on: June 19, 2023 20:41:13

Created with - SDNIST v2.2.1 + SDNIST v2.3.0

@@ -275,7 +275,52 @@

- Library + Variant Label + + + + default CTGAN with epochs=500 + + + + + + + + + + + Submission Number + + + + 1 + + + + + + + + + + + Algorithm Type + + + + neural net + + + + + + + + + + + Library Name @@ -290,7 +335,7 @@

- Feature Set + Feature Set Name @@ -305,11 +350,11 @@

- Variant Label + Privacy Category - default CTGAN with epochs=500 + non_dp @@ -320,11 +365,41 @@

- Submission Number + Deid Data Id - 1 + 42faa7686f69e7468a6db213850ad41924530454 + + + + + + + + + + + Features List + + + + PUMA, AGEP, SEX, MSP, HISP, RAC1P, NOC, NPF, HOUSING_TYPE, OWN_RENT, DENSITY, INDP, INDP_CAT, EDU, PINCP, PINCP_DECILE, POVPIP, DVET, DREM, DPHY, DEYE, DEAR, WGTP, PWGTP + + + + + + + + + + + Privacy Label Detail + + + + No privacy guarantee used @@ -335,11 +410,11 @@

- Privacy + Research Papers - Synthetic Data (Non-differentially Private) + https://doi.org/10.48550/arXiv.1907.00503 @@ -1242,7 +1317,7 @@

- K-Marginal score of the deidentified data closely resembles K-Marginal score of a 10% sub-sample of the target data. + K-Marginal score of the deidentified data closely resembles K-Marginal score of a 5% sub-sample of the target data.
@@ -1274,29 +1349,50 @@

- + Sub-Sample Size - + Sub-Sample K-Marginal Score - + Deidentified Data K-marginal score - + Absolute Diff. From Deidentified Data K-marginal Score + + + + + + + + + 1% + + + + 565 + + + + 683 + + + + 118 @@ -1307,11 +1403,11 @@

- 10% + 5% - 843 + 779 @@ -1319,7 +1415,7 @@

- 160 + 96 @@ -1333,11 +1429,11 @@

- 20% + 10% - 892 + 839 @@ -1345,7 +1441,7 @@

- 209 + 156 @@ -1356,11 +1452,11 @@

- 30% + 20% - 917 + 891 @@ -1368,7 +1464,7 @@

- 234 + 208 @@ -1379,11 +1475,11 @@

- 40% + 30% - 932 + 916 @@ -1391,7 +1487,7 @@

- 249 + 233 @@ -1402,11 +1498,11 @@

- 50% + 40% - 946 + 933 @@ -1414,7 +1510,7 @@

- 263 + 250 @@ -1425,11 +1521,11 @@

- 60% + 50% - 954 + 946 @@ -1437,7 +1533,7 @@

- 271 + 263 @@ -1448,11 +1544,11 @@

- 70% + 60% - 964 + 956 @@ -1460,7 +1556,7 @@

- 281 + 273 @@ -1471,11 +1567,11 @@

- 80% + 70% - 972 + 965 @@ -1483,7 +1579,7 @@

- 289 + 282 @@ -1494,11 +1590,11 @@

- 90% + 80% - 981 + 973 @@ -1506,7 +1602,7 @@

- 298 + 290 @@ -1517,11 +1613,11 @@

- 100% + 90% - 1000 + 982 @@ -1529,7 +1625,7 @@

- 317 + 299 @@ -1633,7 +1729,7 @@

- 853 + 856 @@ -1655,7 +1751,7 @@

- 831 + 838 @@ -1677,7 +1773,7 @@

- 880 + 888 @@ -1699,7 +1795,7 @@

- 850 + 853 @@ -1721,7 +1817,7 @@

- 821 + 814 @@ -1743,7 +1839,7 @@

- 854 + 864 @@ -1765,7 +1861,7 @@

- 855 + 856 @@ -1787,7 +1883,7 @@

- 841 + 840 @@ -1809,7 +1905,7 @@

- 867 + 870 @@ -1831,7 +1927,7 @@

- 872 + 875 @@ -1853,7 +1949,7 @@

- 839 + 837 @@ -1875,7 +1971,7 @@

- 849 + 855 @@ -1897,7 +1993,7 @@

- 900 + 901 @@ -1919,7 +2015,7 @@

- 853 + 852 @@ -1963,7 +2059,7 @@

- 860 + 859 @@ -2007,7 +2103,7 @@

- 870 + 877 @@ -2029,7 +2125,7 @@

- 860 + 857 @@ -2051,7 +2147,7 @@

- 891 + 893 @@ -5271,7 +5367,7 @@

- This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you’re using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes– the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive. + This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you're using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.
@@ -5554,7 +5650,7 @@

- The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP = “N”, individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. + The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP='N', individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape.
@@ -5664,6 +5760,34 @@

+
+ +
+ + + + +
+ In real world tabular data, it's common for record features to have some deterministic, publicly known dependencies on each other: knowing someone's AGEP= 3 necessarily tells you something about their marital status, income and educational attainment. Different deidentification methods may be better or worse at automatically preserving these relationships. When they fail (ex: producing toddlers with PhDs) we say those records contain "inconsistencies". Our consistency check metric below is not exhaustive, we don't catch everything the way a production-grade system should, but this will give you a sense of how consistency is preserved for age, work and household features. Note that different deidentification approaches may do better or worse with different types of inconsistencies. +
+ + + +
+ + + + + + + + + + + + + +

@@ -17261,7 +17385,7 @@

- K-Marginal Score Breakdown: + Worst Performing PUMAs Breakdown:

@@ -17285,7 +17409,7 @@

- In the metrics above we’ve considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score. + In the metrics above we've considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score.
@@ -17384,7 +17508,7 @@

- 853 + 856 @@ -17406,7 +17530,7 @@

- 831 + 838 @@ -17428,7 +17552,7 @@

- 880 + 888 @@ -17450,7 +17574,7 @@

- 850 + 853 @@ -17472,7 +17596,7 @@

- 821 + 814 @@ -17952,6 +18076,7 @@

Privacy Evaluation

+

@@ -17979,7 +18104,91 @@

- This is a count of unique records in the target data that were exactly reproduced in the deidentified data. Because these records were unique outliers in the target data, and they still appear unchanged in the deidentified data, they are potentially vulnerable to reidentification. + Unique Exact Match (UEM) is a simple privacy metric that counts the percentage of singleton records in the target that are also present in the deidentified data; these uniquely identifiable individuals leaked through the deidentification process. +
+ + + +

+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ Below we also include an estimate of the feature space size. The feature space is the set of all possible record values given the selected target data and feature subset. For instance, if we had two features, Hat [cap, bonnet] and Color [green, blue, purple], our feature space would consist of 2 x 3 = 6 possible combinations (e.g. 'green cap', 'blue bonnet'). Note that feature spaces are based on the feature set, not on what records actually exist in the data. Purple bonnet is a possible combination in this feature space, but it's likely no one in the hypothetical data owns a purple bonnet (and the count of that record value would be 0). +
+ + + +
+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ As we add features to the feature set, we increase the size of the feature space, but we don't change the actual number of records in the data-- it's the same people, but now they're spread out more thinly across a wider set of possible record values. Large feature spaces will disperse populations very sparsely (most possible record values will have count 0 or 1) and as a result the data will contain very many uniquely identifiable records. Intuitively, once you know enough pieces of information about someone, everyone becomes very distinct from everyone else. This can pose a challenge for privacy. +
+ + + +
+ + + + + + + + + + + + + + +
+ +
+ + + + +
+ The Target Data Properties below provides an estimate of the feature space size (100 is used for continuous feature), along with the portion of the records in the ground truth target data that are unique (ie, they are the only person with that record value, they have a count of 1). The Deidentified Data Properties reports the percentage of those uniquely identifiable individuals that are still present in the deidentified data. Because they are unique, real records, they are potentially vulnerable to reidentification.
@@ -18023,7 +18232,7 @@

- 3,407,717,673,360,000 + 5.178e+26 @@ -18112,6 +18321,12 @@

+
+
+
+
+ +

@@ -18202,7 +18417,7 @@

- HISP, PUMA, OWN_RENT, EDU, MSP, SEX, RAC1P, INDP_CAT + PUMA, INDP_CAT, HISP, MSP, RAC1P, SEX, EDU, OWN_RENT
@@ -21076,6 +21291,34 @@

+
+ +
+ + + + +
+ There are a total of 271 possible codes for INDP, 269 of these codes appear in the Diverse Community Data Excerpts (233 in MA, 264 in Texas and National) +
+ + + +
+ + + + + + + + + + + + + +

diff --git a/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_05-19-2023T18.01.12/ui.json b/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_national2019_06-19-2023T20.33.47/ui.json similarity index 95% rename from sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_05-19-2023T18.01.12/ui.json rename to sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_national2019_06-19-2023T20.33.47/ui.json index 8aa21be..8e5ed28 100644 --- a/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_05-19-2023T18.01.12/ui.json +++ b/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_national2019_06-19-2023T20.33.47/ui.json @@ -1,6 +1,6 @@ { - "Created on": "May 19, 2023 18:04:40", - "version": "2.2.1", + "Created on": "June 19, 2023 20:41:13", + "version": "2.3.0", "data_description": { "target": [ { @@ -55,24 +55,44 @@ "Label Value": "ctgan" }, { - "Label Name": "Library", + "Label Name": "Variant Label", + "Label Value": "default CTGAN with epochs=500" + }, + { + "Label Name": "Submission Number", + "Label Value": 1 + }, + { + "Label Name": "Algorithm Type", + "Label Value": "neural net" + }, + { + "Label Name": "Library Name", "Label Value": "sdv" }, { - "Label Name": "Feature Set", + "Label Name": "Feature Set Name", "Label Value": "all-features" }, { - "Label Name": "Variant Label", - "Label Value": "default CTGAN with epochs=500" + "Label Name": "Privacy Category", + "Label Value": "non_dp" }, { - "Label Name": "Submission Number", - "Label Value": 1 + "Label Name": "Deid Data Id", + "Label Value": "42faa7686f69e7468a6db213850ad41924530454" + }, + { + "Label Name": "Features List", + "Label Value": "PUMA, AGEP, SEX, MSP, HISP, RAC1P, NOC, NPF, HOUSING_TYPE, OWN_RENT, DENSITY, INDP, INDP_CAT, EDU, PINCP, PINCP_DECILE, POVPIP, DVET, DREM, DPHY, DEYE, DEAR, WGTP, PWGTP" }, { - "Label Name": "Privacy", - "Label Value": "Synthetic Data (Non-differentially Private)" + "Label Name": "Privacy Label Detail", + "Label Value": "No privacy guarantee used" + }, + { + "Label Name": "Research Papers", + "Label Value": "https://doi.org/10.48550/arXiv.1907.00503" } ], "validations": [] @@ -250,7 +270,7 @@ }, { "name": null, - "data": "K-Marginal score of the deidentified data closely resembles K-Marginal score of a 10% sub-sample of the target data.", + "data": "K-Marginal score of the deidentified data closely resembles K-Marginal score of a 5% sub-sample of the target data.", "type": "string", "dotted_break": false }, @@ -258,29 +278,41 @@ "name": null, "data": [ { - "Sub-Sample Size": "10%", - "Sub-Sample K-Marginal Score": 843, + "Sub-Sample Size": "1%", + "Sub-Sample K-Marginal Score": 565, + "Deidentified Data K-marginal score": 683, + "Absolute Diff. From Deidentified Data K-marginal Score": "118" + }, + { + "Sub-Sample Size": "5%", + "Sub-Sample K-Marginal Score": 779, "Deidentified Data K-marginal score": 683, - "Absolute Diff. From Deidentified Data K-marginal Score": "160", + "Absolute Diff. From Deidentified Data K-marginal Score": "96", "min_idx": true }, + { + "Sub-Sample Size": "10%", + "Sub-Sample K-Marginal Score": 839, + "Deidentified Data K-marginal score": 683, + "Absolute Diff. From Deidentified Data K-marginal Score": "156" + }, { "Sub-Sample Size": "20%", - "Sub-Sample K-Marginal Score": 892, + "Sub-Sample K-Marginal Score": 891, "Deidentified Data K-marginal score": 683, - "Absolute Diff. From Deidentified Data K-marginal Score": "209" + "Absolute Diff. From Deidentified Data K-marginal Score": "208" }, { "Sub-Sample Size": "30%", - "Sub-Sample K-Marginal Score": 917, + "Sub-Sample K-Marginal Score": 916, "Deidentified Data K-marginal score": 683, - "Absolute Diff. From Deidentified Data K-marginal Score": "234" + "Absolute Diff. From Deidentified Data K-marginal Score": "233" }, { "Sub-Sample Size": "40%", - "Sub-Sample K-Marginal Score": 932, + "Sub-Sample K-Marginal Score": 933, "Deidentified Data K-marginal score": 683, - "Absolute Diff. From Deidentified Data K-marginal Score": "249" + "Absolute Diff. From Deidentified Data K-marginal Score": "250" }, { "Sub-Sample Size": "50%", @@ -290,33 +322,27 @@ }, { "Sub-Sample Size": "60%", - "Sub-Sample K-Marginal Score": 954, + "Sub-Sample K-Marginal Score": 956, "Deidentified Data K-marginal score": 683, - "Absolute Diff. From Deidentified Data K-marginal Score": "271" + "Absolute Diff. From Deidentified Data K-marginal Score": "273" }, { "Sub-Sample Size": "70%", - "Sub-Sample K-Marginal Score": 964, + "Sub-Sample K-Marginal Score": 965, "Deidentified Data K-marginal score": 683, - "Absolute Diff. From Deidentified Data K-marginal Score": "281" + "Absolute Diff. From Deidentified Data K-marginal Score": "282" }, { "Sub-Sample Size": "80%", - "Sub-Sample K-Marginal Score": 972, + "Sub-Sample K-Marginal Score": 973, "Deidentified Data K-marginal score": 683, - "Absolute Diff. From Deidentified Data K-marginal Score": "289" + "Absolute Diff. From Deidentified Data K-marginal Score": "290" }, { "Sub-Sample Size": "90%", - "Sub-Sample K-Marginal Score": 981, - "Deidentified Data K-marginal score": 683, - "Absolute Diff. From Deidentified Data K-marginal Score": "298" - }, - { - "Sub-Sample Size": "100%", - "Sub-Sample K-Marginal Score": 1000, + "Sub-Sample K-Marginal Score": 982, "Deidentified Data K-marginal score": 683, - "Absolute Diff. From Deidentified Data K-marginal Score": "317" + "Absolute Diff. From Deidentified Data K-marginal Score": "299" } ], "type": "table", @@ -337,7 +363,7 @@ "San Francisco County (North & East)--North Beach & Chinatown", "https://censusreporter.org/profiles/79500US0607502-san-francisco-county-north-east-north-beach-chinatown-puma-ca/" ], - "40% Target Subsample Baseline": 853, + "40% Target Subsample Baseline": 856, "Deidentified Data Score": 123 }, { @@ -346,7 +372,7 @@ "NYC-Bronx Community District 1 & 2--Hunts Point, Longwood & Melrose", "https://censusreporter.org/profiles/79500US3603710-nyc-bronx-community-district-1-2-hunts-point-longwood-melrose-puma-ny/" ], - "40% Target Subsample Baseline": 831, + "40% Target Subsample Baseline": 838, "Deidentified Data Score": 192 }, { @@ -355,7 +381,7 @@ "East Montana (Outside Billings City)", "https://censusreporter.org/profiles/79500US3000600-east-montana-outside-billings-city-puma-mt/" ], - "40% Target Subsample Baseline": 880, + "40% Target Subsample Baseline": 888, "Deidentified Data Score": 212 }, { @@ -364,7 +390,7 @@ "NYC-Brooklyn Community District 17--East Flatbush, Farragut & Rugby", "https://censusreporter.org/profiles/79500US3604010-nyc-brooklyn-community-district-17-east-flatbush-farragut-rugby-puma-ny/" ], - "40% Target Subsample Baseline": 850, + "40% Target Subsample Baseline": 853, "Deidentified Data Score": 231 }, { @@ -373,7 +399,7 @@ "Birmingham City (West)", "https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/" ], - "40% Target Subsample Baseline": 821, + "40% Target Subsample Baseline": 814, "Deidentified Data Score": 239 }, { @@ -382,7 +408,7 @@ "Atlanta Regional Commission--Fulton County (Central)--Atlanta City (Central)", "https://censusreporter.org/profiles/79500US1304600-atlanta-regional-commission-fulton-county-central-atlanta-city-central-puma-ga/" ], - "40% Target Subsample Baseline": 854, + "40% Target Subsample Baseline": 864, "Deidentified Data Score": 283 }, { @@ -391,7 +417,7 @@ "Las Vegas City (Southeast)", "https://censusreporter.org/profiles/79500US3200405-las-vegas-city-southeast-puma-nv/" ], - "40% Target Subsample Baseline": 855, + "40% Target Subsample Baseline": 856, "Deidentified Data Score": 303 }, { @@ -400,7 +426,7 @@ "St. Louis City (North)", "https://censusreporter.org/profiles/79500US2901901-st-louis-city-north-puma-mo/" ], - "40% Target Subsample Baseline": 841, + "40% Target Subsample Baseline": 840, "Deidentified Data Score": 326 }, { @@ -409,7 +435,7 @@ "Boulder County (Central)--Boulder City", "https://censusreporter.org/profiles/79500US0800803-boulder-county-central-boulder-city-puma-co/" ], - "40% Target Subsample Baseline": 867, + "40% Target Subsample Baseline": 870, "Deidentified Data Score": 358 }, { @@ -418,7 +444,7 @@ "Alexandria City", "https://censusreporter.org/profiles/79500US5151255-alexandria-city-puma-va/" ], - "40% Target Subsample Baseline": 872, + "40% Target Subsample Baseline": 875, "Deidentified Data Score": 359 }, { @@ -427,7 +453,7 @@ "Chicago City (South)--Auburn Gresham, Roseland, Chatham, Avalon Park & Burnside", "https://censusreporter.org/profiles/79500US1703531-chicago-city-south-auburn-gresham-roseland-chatham-avalon-park-burnside-puma-il/" ], - "40% Target Subsample Baseline": 839, + "40% Target Subsample Baseline": 837, "Deidentified Data Score": 404 }, { @@ -436,7 +462,7 @@ "Cherokee, Sequoyah & Adair Counties", "https://censusreporter.org/profiles/79500US4000200-cherokee-sequoyah-adair-counties-puma-ok/" ], - "40% Target Subsample Baseline": 849, + "40% Target Subsample Baseline": 855, "Deidentified Data Score": 432 }, { @@ -445,7 +471,7 @@ "Montgomery County (South)--Bethesda, Potomac & North Bethesda", "https://censusreporter.org/profiles/79500US2401004-montgomery-county-south-bethesda-potomac-north-bethesda-puma-md/" ], - "40% Target Subsample Baseline": 900, + "40% Target Subsample Baseline": 901, "Deidentified Data Score": 434 }, { @@ -454,7 +480,7 @@ "Central Region--Jackson City (East & Central)", "https://censusreporter.org/profiles/79500US2801100-central-region-jackson-city-east-central-puma-ms/" ], - "40% Target Subsample Baseline": 853, + "40% Target Subsample Baseline": 852, "Deidentified Data Score": 440 }, { @@ -472,7 +498,7 @@ "Washtenaw County (East Central)--Ann Arbor City Area", "https://censusreporter.org/profiles/79500US2602702-washtenaw-county-east-central-ann-arbor-city-area-puma-mi/" ], - "40% Target Subsample Baseline": 860, + "40% Target Subsample Baseline": 859, "Deidentified Data Score": 487 }, { @@ -490,7 +516,7 @@ "Chicago City (South)--South Shore, Hyde Park, Woodlawn, Grand Boulevard & Douglas", "https://censusreporter.org/profiles/79500US1703529-chicago-city-south-south-shore-hyde-park-woodlawn-grand-boulevard-douglas-puma-il/" ], - "40% Target Subsample Baseline": 870, + "40% Target Subsample Baseline": 877, "Deidentified Data Score": 491 }, { @@ -499,7 +525,7 @@ "Des Moines City", "https://censusreporter.org/profiles/79500US1901700-des-moines-city-puma-ia/" ], - "40% Target Subsample Baseline": 860, + "40% Target Subsample Baseline": 857, "Deidentified Data Score": 516 }, { @@ -508,7 +534,7 @@ "West North Dakota--Minot City", "https://censusreporter.org/profiles/79500US3800100-west-north-dakota-minot-city-puma-nd/" ], - "40% Target Subsample Baseline": 891, + "40% Target Subsample Baseline": 893, "Deidentified Data Score": 547 } ], @@ -1453,7 +1479,7 @@ "-1": [ { "name": null, - "data": "This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you\u2019re using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes\u2013 the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.", + "data": "This is another approach for visualizing where the distribution of the deidentified data has shifted away from the target data. In this approach, we begin by using Principle Component Analysis to find a way of representing the target data in a lower dimensional space (in 5 dimensions rather than the full 22 dimensions of the original feature space). Descriptions of these new five dimensions (components) are given in the components table; the components will change depending on which target data set you're using. Five dimensions are better than 22, but we actually want to get down to two dimensions so we can plot the data on simple (x,y) axes the plots below show the data across each possible pair combination of our five components. You can compare how the shapes change between the target data and the deidentified data, and consider what that might mean in light of the component definitions. This is a relatively new visualization metric that was introduced by the IPUMS International team during the HLG-MOS Synthetic Data Test Drive.", "type": "string", "dotted_break": false }, @@ -1520,7 +1546,7 @@ }, { "name": null, - "data": "The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP = \u201cN\u201d, individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. ", + "data": "The queries below explore the PCA metric results in more detail by zooming in on a single component-pair panel and highlighting all individuals that satisfy a given constraint (such as MSP='N', individuals who are unmarried because they are children). If the deidentified data preserves the structure and feature correlations of the target data, the highlighted areas should have similar shape. ", "type": "string", "dotted_break": false }, @@ -1552,6 +1578,12 @@ "metric_name": "Inconsistencies", "attachments": { "-1": [ + { + "name": null, + "data": "In real world tabular data, it's common for record features to have some deterministic, publicly known dependencies on each other: knowing someone's AGEP= 3 necessarily tells you something about their marital status, income and educational attainment. Different deidentification methods may be better or worse at automatically preserving these relationships. When they fail (ex: producing toddlers with PhDs) we say those records contain \"inconsistencies\". Our consistency check metric below is not exhaustive, we don't catch everything the way a production-grade system should, but this will give you a sense of how consistency is preserved for age, work and household features. Note that different deidentification approaches may do better or worse with different types of inconsistencies.", + "type": "string", + "dotted_break": false + }, { "name": "Summary", "data": { @@ -2804,12 +2836,12 @@ } }, { - "metric_name": "K-Marginal Score Breakdown", + "metric_name": "Worst Performing PUMAs Breakdown", "attachments": { "-1": [ { "name": null, - "data": "In the metrics above we\u2019ve considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score.", + "data": "In the metrics above we've considered all of the data together; however we know that algorithms may behave differently on different subgroups in the population. Below we look in more detail at deidentification performance just in the worst performing PUMA, based on k-marginal score.", "type": "string", "dotted_break": false }, @@ -2828,7 +2860,7 @@ "San Francisco County (North & East)--North Beach & Chinatown", "https://censusreporter.org/profiles/79500US0607502-san-francisco-county-north-east-north-beach-chinatown-puma-ca/" ], - "40% Target Subsample Baseline": 853, + "40% Target Subsample Baseline": 856, "Deidentified Data Score": 123 }, { @@ -2837,7 +2869,7 @@ "NYC-Bronx Community District 1 & 2--Hunts Point, Longwood & Melrose", "https://censusreporter.org/profiles/79500US3603710-nyc-bronx-community-district-1-2-hunts-point-longwood-melrose-puma-ny/" ], - "40% Target Subsample Baseline": 831, + "40% Target Subsample Baseline": 838, "Deidentified Data Score": 192 }, { @@ -2846,7 +2878,7 @@ "East Montana (Outside Billings City)", "https://censusreporter.org/profiles/79500US3000600-east-montana-outside-billings-city-puma-mt/" ], - "40% Target Subsample Baseline": 880, + "40% Target Subsample Baseline": 888, "Deidentified Data Score": 212 }, { @@ -2855,7 +2887,7 @@ "NYC-Brooklyn Community District 17--East Flatbush, Farragut & Rugby", "https://censusreporter.org/profiles/79500US3604010-nyc-brooklyn-community-district-17-east-flatbush-farragut-rugby-puma-ny/" ], - "40% Target Subsample Baseline": 850, + "40% Target Subsample Baseline": 853, "Deidentified Data Score": 231 }, { @@ -2864,7 +2896,7 @@ "Birmingham City (West)", "https://censusreporter.org/profiles/79500US0101301-birmingham-city-west-puma-al/" ], - "40% Target Subsample Baseline": 821, + "40% Target Subsample Baseline": 814, "Deidentified Data Score": 239 } ], @@ -2989,13 +3021,31 @@ "-1": [ { "name": null, - "data": "This is a count of unique records in the target data that were exactly reproduced in the deidentified data. Because these records were unique outliers in the target data, and they still appear unchanged in the deidentified data, they are potentially vulnerable to reidentification.", + "data": "Unique Exact Match (UEM) is a simple privacy metric that counts the percentage of singleton records in the target that are also present in the deidentified data; these uniquely identifiable individuals leaked through the deidentification process.", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "Below we also include an estimate of the feature space size. The feature space is the set of all possible record values given the selected target data and feature subset. For instance, if we had two features, Hat [cap, bonnet] and Color [green, blue, purple], our feature space would consist of 2 x 3 = 6 possible combinations (e.g. 'green cap', 'blue bonnet'). Note that feature spaces are based on the feature set, not on what records actually exist in the data. Purple bonnet is a possible combination in this feature space, but it's likely no one in the hypothetical data owns a purple bonnet (and the count of that record value would be 0).", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "As we add features to the feature set, we increase the size of the feature space, but we don't change the actual number of records in the data-- it's the same people, but now they're spread out more thinly across a wider set of possible record values. Large feature spaces will disperse populations very sparsely (most possible record values will have count 0 or 1) and as a result the data will contain very many uniquely identifiable records. Intuitively, once you know enough pieces of information about someone, everyone becomes very distinct from everyone else. This can pose a challenge for privacy. ", + "type": "string", + "dotted_break": false + }, + { + "name": null, + "data": "The Target Data Properties below provides an estimate of the feature space size (100 is used for continuous feature), along with the portion of the records in the ground truth target data that are unique (ie, they are the only person with that record value, they have a count of 1). The Deidentified Data Properties reports the percentage of those uniquely identifiable individuals that are still present in the deidentified data. Because they are unique, real records, they are potentially vulnerable to reidentification.", "type": "string", "dotted_break": false }, { "name": "Target Data Properties", - "data": "Feature space size (possible combinations): -Highlight-3,407,717,673,360,000-Highlight-
Number of unique records in Target Data: -Highlight-27159 (99.66%-Highlight-)", + "data": "Feature space size (possible combinations): -Highlight-5.178e+26-Highlight-
Number of unique records in Target Data: -Highlight-27159 (99.66%-Highlight-)", "type": "string", "dotted_break": false }, @@ -3026,7 +3076,7 @@ }, { "name": null, - "data": "HISP, PUMA, OWN_RENT, EDU, MSP, SEX, RAC1P, INDP_CAT", + "data": "PUMA, INDP_CAT, HISP, MSP, RAC1P, SEX, EDU, OWN_RENT", "type": "string", "dotted_break": false }, @@ -3642,6 +3692,12 @@ "type": "string", "dotted_break": false }, + { + "name": null, + "data": "There are a total of 271 possible codes for INDP, 269 of these codes appear in the Diverse Community Data Excerpts (233 in MA, 264 in Texas and National)", + "type": "string", + "dotted_break": false + }, { "name": "INDP_CAT: Industry categories", "data": [ diff --git a/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_05-19-2023T18.01.12/univariate/AGEP.jpg b/sdnist/report/sample-reports/report_sdv_ctgan_epochs500_SlokomManel_1_national2019_06-19-2023T20.33.47/univariate/AGEP.jpg similarity index 100% rename from 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b/sdnist/report/score/privacy.py @@ -66,22 +66,15 @@ def privacy_score(dataset: Dataset, ui_data: ReportUIData, report_data, log: Sim log.msg('Apparent Match Distribution', level=3) quasi_idf = [] # list of quasi-identifier features excluded = [] # list of excluded features from apparent match computation - if ds.challenge == CENSUS: - quasi_idf = ['SEX', 'MSP', 'RAC1P', 'OWN_RENT', 'EDU', 'PUMA', 'INDP_CAT', 'HISP'] - quasi_idf = list(set(ds.features).intersection(set(quasi_idf))) - excluded = [] - amd_plot = ApparentMatchDistributionPlot(ds.c_synthetic_data, - ds.c_target_data, - r_ui_d.output_directory, - quasi_idf, - excluded) - amd_plot_paths = amd_plot.save() - rd.add('apparent_match_distribution', amd_plot.report_data) - else: - raise Exception(f'Unknown challenge type: {ds.challenge}') - - rel_cdp_saved_file_paths = ["/".join(list(p.parts)[-2:]) - for p in amd_plot_paths] + use_apparent_match = True + quasi_idf = ['SEX', 'MSP', 'RAC1P', 'OWN_RENT', 'EDU', 'PUMA', 'INDP_CAT', 'HISP'] + quasi_idf = list(set(ds.features).intersection(set(quasi_idf))) + if len(quasi_idf) == 0: + log.msg('No quasi-identifier feature found in the dataset. Skipping Apparent Match Distribution.', + level=2, + timed=False, + msg_type='error') + use_apparent_match = False amd_para_a = Attachment(name=None, _data=app_match_para, @@ -93,36 +86,56 @@ def privacy_score(dataset: Dataset, ui_data: ReportUIData, report_data, log: Sim quasi_list_atch = Attachment(name=None, _data=', '.join(quasi_idf), _type=AttachmentType.String) - # Total rows matched on quasi-identifiers as attachment - rec_matched = amd_plot.quasi_matched_df.shape[0] - rec_percent = round(rec_matched/ds.c_target_data.shape[0] * 100, 2) - rec_mat_para_a = Attachment(name='Records Matched on Quasi-Identifiers', - _data=rec_matched_para, + if use_apparent_match: + excluded = [] + amd_plot = ApparentMatchDistributionPlot(ds.c_synthetic_data, + ds.c_target_data, + r_ui_d.output_directory, + quasi_idf, + excluded) + amd_plot_paths = amd_plot.save() + rd.add('apparent_match_distribution', amd_plot.report_data) + + rel_cdp_saved_file_paths = ["/".join(list(p.parts)[-2:]) + for p in amd_plot_paths] + + # Total rows matched on quasi-identifiers as attachment + rec_matched = amd_plot.quasi_matched_df.shape[0] + rec_percent = round(rec_matched/ds.c_target_data.shape[0] * 100, 2) + + rec_mat_para_a = Attachment(name='Records Matched on Quasi-Identifiers', + _data=rec_matched_para, + _type=AttachmentType.String) + total_quasi_matched = Attachment(name=None, + _data=f"Number of Target Data records exactly matched " + f"in Deid. Data on Quasi-Identifiers: " + f"-Highlight-{rec_matched} ({rec_percent}%)-Highlight-", + _type=AttachmentType.String) + # Apparent match distribution plot as attachment + adp_para_a = Attachment(name='Percentage Similarity of the Matched Records', + _data=percn_matched_para, _type=AttachmentType.String) - total_quasi_matched = Attachment(name=None, - _data=f"Number of Target Data records exactly matched " - f"in Deid. Data on Quasi-Identifiers: " - f"-Highlight-{rec_matched} ({rec_percent}%)-Highlight-", - _type=AttachmentType.String) - # Apparent match distribution plot as attachment - adp_para_a = Attachment(name='Percentage Similarity of the Matched Records', - _data=percn_matched_para, - _type=AttachmentType.String) - adp = Attachment(name=None, - _data=[{IMAGE_NAME: Path(p).stem, PATH: p} - for p in rel_cdp_saved_file_paths], - _type=AttachmentType.ImageLinks) - r_ui_d.add(PrivacyScorePacket("Apparent Match Distribution", - None, - [amd_para_a, - quasi_para_a, - quasi_list_atch, - rec_mat_para_a, - total_quasi_matched, - adp_para_a, - adp])) - log.end_msg() + adp = Attachment(name=None, + _data=[{IMAGE_NAME: Path(p).stem, PATH: p} + for p in rel_cdp_saved_file_paths], + _type=AttachmentType.ImageLinks) + r_ui_d.add(PrivacyScorePacket("Apparent Match Distribution", + None, + [amd_para_a, + quasi_para_a, + quasi_list_atch, + rec_mat_para_a, + total_quasi_matched, + adp_para_a, + adp])) + else: + r_ui_d.add(PrivacyScorePacket("Apparent Match Distribution", + None, + [amd_para_a, + quasi_para_a, + quasi_list_atch])) + log.end_msg() return r_ui_d, rd diff --git a/sdnist/version.py b/sdnist/version.py index 55e4709..8f1ef02 100644 --- a/sdnist/version.py +++ b/sdnist/version.py @@ -1 +1 @@ -__version__ = "2.3.0" +__version__ = "2.4"