Search Results for author: Théo Ryffel

Found 6 papers, 3 papers with code

Differential Privacy Guarantees for Stochastic Gradient Langevin Dynamics

no code implementations28 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.

ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing

1 code implementation8 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.

Federated Learning Privacy Preserving +1

Partially Encrypted Deep Learning using Functional Encryption

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.

BIG-bench Machine Learning Privacy Preserving

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