5 papers
Toward provably private learning from federated data
Katharine Daly, Yu Xiao, Zachary Garrett +9
Federated Learning (FL) allows devices with private data to collaborate in training a shared model. We present a next-generation FL system based on Trusted Execution Environments (…
Advancing the State-of-the-Art in Empirical Privacy Auditing
Nicole Mitchell, Galen Andrew, Arun Ganesh +2
Parameter-efficient fine-tuning of large language models (LLMs) can exhibit problematic memorization of individual training examples. Empirical privacy auditing (EPA) quantifies th…
On Design Principles for Private Adaptive Optimizers
Arun Ganesh, Brendan McMahan, Abhradeep Thakurta
The spherical noise added to gradients in differentially private (DP) training undermines the performance of adaptive optimizers like AdaGrad and Adam, and hence many recent works…
Hush! Protecting Secrets During Model Training: An Indistinguishability Approach
Arun Ganesh, Brendan McMahan, Milad Nasr +2
We consider the problem of secret protection, in which a business or organization wishes to train a model on their own data, while attempting to not leak secrets potentially contai…
Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints
Virat Shejwalkar, Arun Ganesh, Rajiv Mathews +5
In this work, we focus on improving the accuracy-variance trade-off for state-of-the-art differentially private machine learning (DP ML) methods. First, we design a general framewo…