523 citations · 1.4k across the 79 of their papers we have counts for
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Privacy Amplification by Decentralization
Edwige Cyffers, Aurélien Bellet
Analyzing data owned by several parties while achieving a good trade-off between utility and privacy is a key challenge in federated learning and analytics. In this work, we introd…
An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging
César Sabater, Aurélien Bellet, Jan Ramon
Learning from data owned by several parties, as in federated learning, raises challenges regarding the privacy guarantees provided to participants and the correctness of the comput…
Design Choices for X-vector Based Speaker Anonymization
Brij Mohan Lal Srivastava, Natalia Tomashenko, Xin Wang +5
The recently proposed x-vector based anonymization scheme converts any input voice into that of a random pseudo-speaker. In this paper, we present a flexible pseudo-speaker selecti…
Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints
Robin Vogel, Aurélien Bellet, Stephan Clémençon
Many applications of AI involve scoring individuals using a learned function of their attributes. These predictive risk scores are then used to take decisions based on whether the…