From 978b16c7de444b7b525ea778919ae6b166cbf9fb Mon Sep 17 00:00:00 2001 From: Benjamin Case Date: Wed, 2 Oct 2024 15:43:40 -0400 Subject: [PATCH] line indents --- api.bs | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/api.bs b/api.bs index 8e2f45a..447935c 100644 --- a/api.bs +++ b/api.bs @@ -1105,10 +1105,10 @@ parameters: 3. the `filterData` value used for selecting relevant events; 4. the `PrivateAttributionLogic` such as last-touch or equal-credit; 5. two sensitivity parameters: `report_global_sensitivity` which is a cap on how much attributed - value can come from this one conversion (e.g. the conversion value) and `query_global_sensitivity` - which is a maximum sensitivity across all reports to be processed the aggregation query. + value can come from this one conversion (e.g. the conversion value) and `query_global_sensitivity` + which is a maximum sensitivity across all reports to be processed the aggregation query. 6. the p-norm to use when bounding the histogram contribution's sensitivity. 1-norm corresponding - to using Laplace noise in aggregation query and 2-norm for Gaussian noise. + to using Laplace noise in aggregation query and 2-norm for Gaussian noise. The algorithm to deduct privacy budget and compute the attributed histogram will first look across epochs for eligible impressions. It will deduct budget from any epoch with eligible @@ -1119,10 +1119,10 @@ Step 1: select relevant impressions within an epoch using the `filterData`. Step 2: For each epoch compute the individual privacy loss of the query following Thm 4 of [[PPA-DP]]. There are three cases * Case 1: If the epoch has no relevant impressions the privacy loss is 0. * Case 2: If the window of epochs contains only a single epoch, the `individual_sensitivity` is the p-norm of attribution function - applied to only the impressions in this epoch. The privacy loss deducted from the epoch's budget is - then `requested_epsilon * individual_sensitivity / query_global_sensitivity`. + applied to only the impressions in this epoch. The privacy loss deducted from the epoch's budget is + then `requested_epsilon * individual_sensitivity / query_global_sensitivity`. * Case 3: If multiple epochs are considered, the privacy loss deducted from the epoch's budget is - `requested_epsilon * report_global_sensitivity / query_global_sensitivity` + `requested_epsilon * report_global_sensitivity / query_global_sensitivity` Step 3: Attempt to deduct the privacy of the epoch, if the filter has sufficient budget the impressions are added to the set to be considered for attribution; otherise, they are dropped.