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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…
cs.CR2021★ 22 cited
Practical and Private (Deep) Learning without Sampling or Shuffling
Peter Kairouz, Brendan McMahan, Shuang Song +3
We consider training models with differential privacy (DP) using mini-batch gradients. The existing state-of-the-art, Differentially Private Stochastic Gradient Descent (DP-SGD), r…