most citedProtecting Data from all Parties: Combining FHE and DP in Federated Learning

7 citations · 7 across the 1 of their papers we have counts for

collaborators

2 papers

cs.CR2022★ 7 cited

Protecting Data from all Parties: Combining FHE and DP in Federated Learning

Arnaud Grivet Sébert, Renaud Sirdey, Oana Stan +1

This paper tackles the problem of ensuring training data privacy in a federated learning context. Relying on Homomorphic Encryption (HE) and Differential Privacy (DP), we propose a…

cs.CR2020

SPEED: Secure, PrivatE, and Efficient Deep learning

Arnaud Grivet Sébert, Rafael Pinot, Martin Zuber +2

We introduce a deep learning framework able to deal with strong privacy constraints. Based on collaborative learning, differential privacy and homomorphic encryption, the proposed…