Samplable Anonymous Aggregation for Private Federated Data Analysis

no code implementations • 27 Jul 2023 • Kunal Talwar, Shan Wang, Audra McMillan, Vojta Jina, Vitaly Feldman, Pansy Bansal, 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

This gap has led to significant interest in the design and implementation of simple cryptographic primitives, that can allow central-like utility guarantees without having to trust a central server.

Federated Learning

Differentially Private Heavy Hitter Detection using Federated Analytics

no code implementations • 21 Jul 2023 • Karan Chadha, Junye Chen, John Duchi, Vitaly Feldman, Hanieh Hashemi, Omid Javidbakht, Audra McMillan, Kunal Talwar

In this work, we study practical heuristics to improve the performance of prefix-tree based algorithms for differentially private heavy hitter detection.

Private Adaptive Gradient Methods for Convex Optimization

no code implementations • 25 Jun 2021 • Hilal Asi, John Duchi, Alireza Fallah, Omid Javidbakht, Kunal Talwar

We study adaptive methods for differentially private convex optimization, proposing and analyzing differentially private variants of a Stochastic Gradient Descent (SGD) algorithm with adaptive stepsizes, as well as the AdaGrad algorithm.

Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications

no code implementations • 16 Feb 2021 • Matthias Paulik, Matt Seigel, Henry Mason, Dominic Telaar, Joris Kluivers, Rogier Van Dalen, Chi Wai Lau, Luke Carlson, Filip Granqvist, Chris Vandevelde, Sudeep Agarwal, Julien Freudiger, Andrew Byde, Abhishek Bhowmick, Gaurav Kapoor, Si Beaumont, Áine Cahill, Dominic Hughes, Omid Javidbakht, Fei Dong, Rehan Rishi, Stanley Hung

We describe the design of our federated task processing system.

Federated Learning

Element Level Differential Privacy: The Right Granularity of Privacy

no code implementations • 5 Dec 2019 • Hilal Asi, John Duchi, Omid Javidbakht

Differential Privacy (DP) provides strong guarantees on the risk of compromising a user's data in statistical learning applications, though these strong protections make learning challenging and may be too stringent for some use cases.