3 papers
cs.LG2026
JAX-Privacy: A library for differentially private machine learning
Ryan McKenna, Galen Andrew, Borja Balle +6
JAX-Privacy is a library designed to simplify the deployment of robust and performant mechanisms for differentially private machine learning. Guided by design principles of usabili…
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…