Private Federated Statistics in an Interactive Setting

Audra McMillan; Omid Javidbakht; Kunal Talwar; Elliot Briggs; Mike Chatzidakis; Junye Chen; John Duchi; Vitaly Feldman; 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

arXiv · 2211.10082

Private Federated Statistics in an Interactive Setting

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Abstract

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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Audra McMillan, Omid Javidbakht, Kunal Talwar, Elliot Briggs, Mike Chatzidakis, Junye Chen, John Duchi, Vitaly Feldman, 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. 2022-11-18. Private Federated Statistics in an Interactive Setting. https://arxiv.org/abs/2211.10082

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