7 citations · 8 across the 2 of their papers we have counts for
4 papers
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…
Revisiting old combinatorial beasts in the quantum age: quantum annealing versus maximal matching
Daniel Vert, Renaud Sirdey, Stéphane Louise
This paper experimentally investigates the behavior of analog quantum computers such as commercialized by D-Wave when confronted to instances of the maximum cardinality matching pr…
A linear programming approach to general dataflow process network verification and dimensioning
Renaud Sirdey, Pascal Aubry
In this paper, we present linear programming-based sufficient conditions, some of them polynomial-time, to establish the liveness and memory boundedness of general dataflow process…