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

7 citations · 7 across the 2 of their papers we have counts for

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cs.CR2026

MOSAIC-FL, a micro-service based privacy-preserving framework with application to genomics

Paul Largillier, Karl Paygambar, Cédric Gouy-Pailler +3

Security and privacy are primordial requirements for Federated Learning (FL), especially in fields such as healthcare and genomics where sensitive information has to be analyzed. O…

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…