23 citations · 58 across the 9 of their papers we have counts for
Showing 2021Show all
2 papers · 1 filter
cs.LG2021★ 23 cited
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…
cs.LG2021★ 8 cited
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.…