18 citations · 25 across the 4 of their papers we have counts for
4 papers
Beyond Theoretical Bounds: Empirical Privacy Loss Calibration for Text Rewriting Under Local Differential Privacy
Weijun Li, Arnaud Grivet Sébert, Qiongkai Xu +2
The growing use of large language models has increased interest in sharing textual data in a privacy-preserving manner. One prominent line of work addresses this challenge through…
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…
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…