3 papers
cs.LG2026
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…
cs.LG2025
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…
cs.CR2025
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…