368 citations · 647 across the 16 of their papers we have counts for
26 papers
Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data?
Marika Swanberg, Ryan McKenna, Edo Roth +2
Differentially private (DP) synthetic data is a versatile tool for enabling the analysis of private data. Recent advancements in large language models (LLMs) have inspired a number…
Improved Communication-Privacy Trade-offs in Mean Estimation under Streaming Differential Privacy
Wei-Ning Chen, Berivan Isik, Peter Kairouz +3
We study mean estimation under central differential privacy and communication constraints, and address two key challenges: firstly, existing mean estimation schemes that simu…
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…
Privacy-Utility Trades in Crowdsourced Signal Map Obfuscation
Jiang Zhang, Lillian Clark, Matthew Clark +2
Cellular providers and data aggregating companies crowdsource celluar signal strength measurements from user devices to generate signal maps, which can be used to improve network p…
The Skellam Mechanism for Differentially Private Federated Learning
Naman Agarwal, Peter Kairouz, Ziyu Liu
We introduce the multi-dimensional Skellam mechanism, a discrete differential privacy mechanism based on the difference of two independent Poisson random variables. To quantify its…
Pointwise Bounds for Distribution Estimation under Communication Constraints
Wei-Ning Chen, Peter Kairouz, Ayfer Özgür
We consider the problem of estimating a -dimensional discrete distribution from its samples observed under a -bit communication constraint. In contrast to most previous resul…