368 citations · 1k across the 44 of their papers we have counts for
6 papers · 1 filter
Private Domain Adaptation from a Public Source
Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh
A key problem in a variety of applications is that of domain adaptation from a public source domain, for which a relatively large amount of labeled data with no privacy constraints…
Algorithms for bounding contribution for histogram estimation under user-level privacy
Yuhan Liu, Ananda Theertha Suresh, Wennan Zhu +2
We study the problem of histogram estimation under user-level differential privacy, where the goal is to preserve the privacy of all entries of any single user. We consider the het…
Differentially Private Learning with Margin Guarantees
Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh
We present a series of new differentially private (DP) algorithms with dimension-independent margin guarantees. For the family of linear hypotheses, we give a pure DP learning algo…
Scaling Language Model Size in Cross-Device Federated Learning
Jae Hun Ro, Theresa Breiner, Lara McConnaughey +4
Most studies in cross-device federated learning focus on small models, due to the server-client communication and on-device computation bottlenecks. In this work, we leverage vario…
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning
Wei-Ning Chen, Christopher A. Choquette-Choo, Peter Kairouz +1
We consider the problem of training a dimensional model with distributed differential privacy (DP) where secure aggregation (SecAgg) is used to ensure that the server only sees…
Correlated quantization for distributed mean estimation and optimization
Ananda Theertha Suresh, Ziteng Sun, Jae Hun Ro +1
We study the problem of distributed mean estimation and optimization under communication constraints. We propose a correlated quantization protocol whose leading term in the error…