Samplable Anonymous Aggregation for Private Federated Data Analysis
Kunal Talwar Shan Wang Audra McMillan Vojta Jina Vitaly Feldman Bailey Basile Aine Cahill Yi Sheng Chan Mike Chatzidakis Junye Chen Oliver Chick Mona Chitnis Suman Ganta Yusuf Goren Filip Granqvist Kristine Guo Frederic Jacobs Omid Javidbakht Albert Liu Richard Low Dan Mascenik Steve Myers David Park Wonhee Park Gianni Parsa Tommy Pauly Christian Priebe Rehan Rishi Guy Rothblum Michael Scaria Linmao Song Congzheng Song Karl Tarbe Sebastian Vogt Luke Winstrom Shundong Zhou
We revisit the problem of designing scalable protocols for private statistics
and private federated learning when each device holds its private data. Our
first contribution is to propose a simple primitive that allows for efficient
implementation of several commonly used algorithms, and allows for privacy
accounting that is close to that in the central setting without requiring the
strong trust assumptions it entails. Second, we propose a system architecture
that implements this primitive and perform a security analysis of the proposed
system.