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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

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 · ORCID
classification cs.CR
keywords privatestatisticsdevicesenablefederatedinteractivesystemuser
verification ladder T0 review T1 audit T2 compute T3 formal

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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 1 Pith paper

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  1. Modular Federated Learning: A Meta-Framework Perspective

    cs.LG 2025-05 conditional novelty 3.0 of 10

    A 63-page survey that reframes federated learning as a composition of eight modules and proposes an 'alignment operator' taxonomy, while surveying Python FL frameworks and open challenges.

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