no code implementations • 28 Jan 2022 • Théo Ryffel, Francis Bach, David Pointcheval
We analyse the privacy leakage of noisy stochastic gradient descent by modeling R\'enyi divergence dynamics with Langevin diffusions.
2 code implementations • 8 Jun 2020 • Théo Ryffel, Pierre Tholoniat, David Pointcheval, Francis Bach
We evaluate our end-to-end system for private inference between distant servers on standard neural networks such as AlexNet, VGG16 or ResNet18, and for private training on smaller networks like LeNet.
1 code implementation • NeurIPS 2019 • Théo Ryffel, David Pointcheval, Francis Bach, Edouard Dufour-Sans, Romain Gay
Machine learning on encrypted data has received a lot of attention thanks to recent breakthroughs in homomorphic encryption and secure multi-party computation.
3 code implementations • 24 May 2019 • Theo Ryffel, Edouard Dufour-Sans, Romain Gay, Francis Bach, David Pointcheval
Machine learning on encrypted data has received a lot of attention thanks to recent breakthroughs in homomorphic encryption and secure multi-party computation.