When approximate design for fast homomorphic computation provides differential privacy guarantees

no code implementations • 6 Apr 2023 • Arnaud Grivet Sébert, Martin Zuber, Oana Stan, Renaud Sirdey, Cédric Gouy-Pailler

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 importance.

Computational Efficiency

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

no code implementations • 9 May 2022 • Arnaud Grivet Sébert, Renaud Sirdey, Oana Stan, Cédric Gouy-Pailler

This paper tackles the problem of ensuring training data privacy in a federated learning context.

Federated Learning Quantization