8 citations · 11 across the 2 of their papers we have counts for
3 papers · 1 filter
Defending against Reconstruction Attacks with Rényi Differential Privacy
Pierre Stock, Igor Shilov, Ilya Mironov +1
Reconstruction attacks allow an adversary to regenerate data samples of the training set using access to only a trained model. It has been recently shown that simple heuristics can…
Opacus: User-Friendly Differential Privacy Library in PyTorch
Ashkan Yousefpour, Igor Shilov, Alexandre Sablayrolles +9
We introduce Opacus, a free, open-source PyTorch library for training deep learning models with differential privacy (hosted at opacus.ai). Opacus is designed for simplicity, flexi…
Antipodes of Label Differential Privacy: PATE and ALIBI
Mani Malek, Ilya Mironov, Karthik Prasad +2
We consider the privacy-preserving machine learning (ML) setting where the trained model must satisfy differential privacy (DP) with respect to the labels of the training examples.…