Zobrazeno 1 - 10
of 212
pro vyhledávání: '"Pointcheval, David"'
Oblivious Transfer (OT) is a major primitive for secure multiparty computation. Indeed, combined with symmetric primitives along with garbled circuits, it allows any secure function evaluation between two parties. In this paper, we propose a new appr
Externí odkaz:
http://arxiv.org/abs/2209.04149
Autor:
Cottier, Baptiste, Pointcheval, David
Ephemeral Diffie-Hellman Over COSE (EDHOC) aims at being a very compact and lightweight authenticated Diffie-Hellman key exchange with ephemeral keys. It is expected to provide mutual authentication, forward secrecy, and identity protection, with a 1
Externí odkaz:
http://arxiv.org/abs/2209.03599
We analyse the privacy leakage of noisy stochastic gradient descent by modeling R\'enyi divergence dynamics with Langevin diffusions. Inspired by recent work on non-stochastic algorithms, we derive similar desirable properties in the stochastic setti
Externí odkaz:
http://arxiv.org/abs/2201.11980
Publikováno v:
ARES 2021 - 16th International Conference on Availability, Reliability and Security, Aug 2021, Vienna, Austria. pp.1-12
Decision forests are classical models to efficiently make decision on complex inputs with multiple features. While the global structure of the trees or forests is public, sensitive information have to be protected during the evaluation of some client
Externí odkaz:
http://arxiv.org/abs/2108.08546
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 (with a trusted dealer) leverages function secret sharing, a recent li
Externí odkaz:
http://arxiv.org/abs/2006.04593
Autor:
Hébant, Chloé, Pointcheval, David
Publikováno v:
In Information and Computation August 2023 293
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 outsourcing computation to untrusted servers without sacrificing privacy of sens
Externí odkaz:
http://arxiv.org/abs/1905.10214