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Private Federated Statistics in an Interactive Setting

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arxiv 2211.10082 v1 pith:TVRUWMYH submitted 2022-11-18 cs.CR

Private Federated Statistics in an Interactive Setting

Audra McMillan , Omid Javidbakht , Kunal Talwar , Elliot Briggs , Mike Chatzidakis , Junye Chen , John Duchi , Vitaly Feldman
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Yusuf Goren Michael Hesse Vojta Jina Anil Katti Albert Liu Cheney Lyford Joey Meyer Alex Palmer David Park Wonhee Park Gianni Parsa Paul Pelzl Rehan Rishi Congzheng Song Shan Wang Shundong Zhou
This is my paper
classification cs.CR
keywords privatestatisticsdevicesenablefederatedinteractivesystemuser
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Privately learning statistics of events on devices can enable improved user experience. Differentially private algorithms for such problems can benefit significantly from interactivity. We argue that an aggregation protocol can enable an interactive private federated statistics system where user's devices maintain control of the privacy assurance. We describe the architecture of such a system, and analyze its security properties.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Auditing Apple's DifferentialPrivacy.framework: Implementation Bugs, Misconfigurations, and Practical Risks

    cs.CR 2026-05 accept novelty 8.0

    Audit finds DP violations in 5 of 9 mechanisms in Apple's framework due to insecure floating-point samplers and disabled local DP in secure aggregation, impacting 87% of macOS Sonoma and 68% of Sequoia data collection.

  2. Auditing Apple's DifferentialPrivacy.framework: Implementation Bugs, Misconfigurations, and Practical Risks

    cs.CR 2026-05 conditional novelty 7.0

    Client-side audit of Apple's closed-source DP framework finds floating-point sampler bugs and misconfigurations that violate DP guarantees in 5 of 9 mechanisms, affecting 87% of data collection on Sonoma and 68% on Sequoia.