7 citations · 14 across the 5 of their papers we have counts for
5 papers
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…
When approximate design for fast homomorphic computation provides differential privacy guarantees
Arnaud Grivet Sébert, Martin Zuber, Oana Stan +2
While machine learning has become pervasive in as diversified fields as industry, healthcare, social networks, privacy concerns regarding the training data have gained a critical i…
Homomorphic Sortition -- Secret Leader Election for PoS Blockchains
Luciano Freitas, Andrei Tonkikh, Adda-Akram Bendoukha +4
In a single secret leader election protocol (SSLE), one of the system participants is chosen and, unless it decides to reveal itself, no other participant can identify it. SSLE has…
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…
RandSolomon: Optimally Resilient Random Number Generator with Deterministic Termination
Luciano Freitas de Souza, Andrei Tonkikh, Sara Tucci-Piergiovanni +4
Multi-party random number generation is a key building-block in many practical protocols. While straightforward to solve when all parties are trusted to behave correctly, the probl…