23 citations · 35 across the 8 of their papers we have counts for
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cs.CR2021★ 7 cited
Sample and Threshold Differential Privacy: Histograms and applications
Akash Bharadwaj, Graham Cormode
Federated analytics seeks to compute accurate statistics from data distributed across users' devices while providing a suitable privacy guarantee and being practically feasible to…
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…