368 citations · 1.5k across the 66 of their papers we have counts for
11 papers · 1 filter
User Inference Attacks on Large Language Models
Nikhil Kandpal, Krishna Pillutla, Alina Oprea +3
Fine-tuning is a common and effective method for tailoring large language models (LLMs) to specialized tasks and applications. In this paper, we study the privacy implications of f…
Federated Heavy Hitter Recovery under Linear Sketching
Adria Gascon, Peter Kairouz, Ziteng Sun +1
Motivated by real-life deployments of multi-round federated analytics with secure aggregation, we investigate the fundamental communication-accuracy tradeoffs of the heavy hitter d…
Private Federated Learning with Autotuned Compression
Enayat Ullah, Christopher A. Choquette-Choo, Peter Kairouz +1
We propose new techniques for reducing communication in private federated learning without the need for setting or tuning compression rates. Our on-the-fly methods automatically ad…
Private Federated Learning in Gboard
Yuanbo Zhang, Daniel Ramage, Zheng Xu +3
This white paper describes recent advances in Gboard(Google Keyboard)'s use of federated learning, DP-Follow-the-Regularized-Leader (DP-FTRL) algorithm, and secure aggregation tech…
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance
Jingfeng Wu, Wennan Zhu, Peter Kairouz +1
In federated frequency estimation (FFE), multiple clients work together to estimate the frequencies of their collective data by communicating with a server that respects the privac…
Unleashing the Power of Randomization in Auditing Differentially Private ML
Krishna Pillutla, Galen Andrew, Peter Kairouz +3
We present a rigorous methodology for auditing differentially private machine learning algorithms by adding multiple carefully designed examples called canaries. We take a first pr…
Federated Learning of Gboard Language Models with Differential Privacy
Zheng Xu, Yanxiang Zhang, Galen Andrew +5
We train language models (LMs) with federated learning (FL) and differential privacy (DP) in the Google Keyboard (Gboard). We apply the DP-Follow-the-Regularized-Leader (DP-FTRL)~\…
Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation
Wei-Ning Chen, Dan Song, Ayfer Ozgur +1
Privacy and communication constraints are two major bottlenecks in federated learning (FL) and analytics (FA). We study the optimal accuracy of mean and frequency estimation (canon…
Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment
Rachel Cummings, Damien Desfontaines, David Evans +21
In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP), with a focus of advancing DP's depl…
Differentially Private Stream Processing at Scale
Bing Zhang, Vadym Doroshenko, Peter Kairouz +6
We design, to the best of our knowledge, the first differentially private (DP) stream aggregation processing system at scale. Our system -- Differential Privacy SQL Pipelines (DP-S…
One-shot Empirical Privacy Estimation for Federated Learning
Galen Andrew, Peter Kairouz, Sewoong Oh +3
Privacy estimation techniques for differentially private (DP) algorithms are useful for comparing against analytical bounds, or to empirically measure privacy loss in settings wher…