335 citations · 580 across the 7 of their papers we have counts for
7 papers
Differential Privacy Guarantees for Stochastic Gradient Langevin Dynamics
Théo Ryffel, Francis Bach, David Pointcheval
We analyse the privacy leakage of noisy stochastic gradient descent by modeling Rényi divergence dynamics with Langevin diffusions. Inspired by recent work on non-stochastic algori…
Syft 0.5: A Platform for Universally Deployable Structured Transparency
Adam James Hall, Madhava Jay, Tudor Cebere +20
We present Syft 0.5, a general-purpose framework that combines a core group of privacy-enhancing technologies that facilitate a universal set of structured transparency systems. Th…
Privacy-preserving medical image analysis
Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel +8
The utilisation of artificial intelligence in medicine and healthcare has led to successful clinical applications in several domains. The conflict between data usage and privacy pr…
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing
Théo Ryffel, Pierre Tholoniat, David Pointcheval +1
We propose AriaNN, a low-interaction privacy-preserving framework for private neural network training and inference on sensitive data. Our semi-honest 2-party computation protocol…
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
Miles Brundage, Shahar Avin, Jasmine Wang +56
With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations an…
Partially Encrypted Machine Learning using Functional Encryption
Theo Ryffel, Edouard Dufour-Sans, Romain Gay +2
Machine learning on encrypted data has received a lot of attention thanks to recent breakthroughs in homomorphic encryption and secure multi-party computation. It allows outsourcin…