Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

A proposal for privacy preserving ad attribution measurement using Prio-like architecture #17

Open
winstrom opened this issue Aug 28, 2023 · 5 comments

Comments

@winstrom
Copy link

We have a proposal to allow measuring attribution of advertisements with privacy guarantees.

We try to build on previous privacy proposals such as Private Click Measurement (PCM) , Interoperable Private Attribution (IPA), and Attribution Reporting API with Aggregatable Reports (ARA). Our goal at each stage is to only transmit the minimum information necessary to perform the attribution measurement and nothing else.

Like PCM, we rely on the user’s device to join an advertisement impression and conversion together. This means that the browser is trusted with the event level information on user interactions and joins them into summaries of attribution represented as histograms. These histograms only contain the attribution value of a conversion rather than a browsing history.

Like IPA, we rely on Multi-Party Computation (MPC) frameworks to cryptographically segment data across multiple computation partners so that no individual organization can track an individual. This system is used for both aggregation and to introduce Differentially Private Noise and ensure that there is a well defined privacy loss bound for each user of the system. We rely on the Prio (Prio | Stanford Applied Crypto Group) framework to perform this multi-party aggregation. We rely on Differential Privacy ((Differential PrivacyThe Algorithmic Foundations of Differential Privacy)) to add appropriate noise to attribution calculations to make them private.

Like ARA, we wish to allow measurements across a large, sparse space defining the potential linkages between advertisers and publishers. We present a concrete way to encode this sparse space using dense histograms so that individual contributions can be aggregated using known MPC approaches.

For a readable overview, we provide an explainer

We also provide a document with more details

@AramZS
Copy link
Contributor

AramZS commented Aug 28, 2023

Hi @winstrom - just to confirm, the "we" here is Apple?

Also, great to have these docs! Could we add them in via a PR?

@winstrom
Copy link
Author

winstrom commented Aug 28, 2023

Hi @AramZS -- yes "we" is Apple WebKit. Sorry for not being clear.

Happy to add via a PR or to add them to a repo in https://github.com/patcg-individual-drafts (see #16)

@johnwilander
Copy link

We try to refer to ourselves as Apple WebKit in the web standards context. My preference would be to use that.

@AramZS
Copy link
Contributor

AramZS commented Aug 29, 2023

@winstrom Ah, I see that request, I have added you as a member to patcg-individual-drafts you should have access there to set up a repo now. Let me know if that doesn't work.

@winstrom
Copy link
Author

Thanks @AramZS -- files have been added to https://github.com/patcg-individual-drafts/private-ad-measurement

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

3 participants