18 citations · 34 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…