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On the Differential Privacy and Interactivity of Privacy Sandbox Reports

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arxiv 2412.16916 v3 pith:WEGOMOEO submitted 2024-12-22 cs.CR

Badih Ghazi , Charlie Harrison , Arpana Hosabettu , Pritish Kamath , Alexander Knop , Ravi Kumar , Ethan Leeman , Pasin Manurangsi
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Mariana Raykova Vikas Sahu Phillipp Schoppmann
This is my paper · ORCID
classification cs.CR
keywords privacyapisdifferentialsandboxabstractadvertisingaggregationanalysis
verification ladder T0 review T1 audit T2 compute T3 formal
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The Privacy Sandbox initiative from Google includes APIs for enabling privacy-preserving advertising functionalities as part of the effort around limiting third-party cookies. In particular, the Private Aggregation API (PAA) and the Attribution Reporting API (ARA) can be used for ad measurement while providing different guardrails for safeguarding user privacy, including a framework for satisfying differential privacy (DP). In this work, we provide an abstract model for analyzing the privacy of these APIs and show that they satisfy a formal DP guarantee under certain assumptions. Our analysis handles the case where both the queries and database can change interactively based on previous responses from the API.

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Cited by 1 Pith paper

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  1. Differentially Private Synthetic Data Release for Topics API Outputs

    cs.CR 2025-06 conditional novelty 6.0 of 10

    The paper presents a differentially private methodology and a public synthetic dataset of Topics API traces that match real re-identification risk within one standard deviation on two attacks.

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