10 citations · 19 across the 8 of their papers we have counts for
4 papers · 1 filter
Differentially Private Training of Mixture of Experts Models
Pierre Tholoniat, Huseyin A. Inan, Janardhan Kulkarni +1
This position paper investigates the integration of Differential Privacy (DP) in the training of Mixture of Experts (MoE) models within the field of natural language processing. As…
DPack: Efficiency-Oriented Privacy Budget Scheduling
Pierre Tholoniat, Kelly Kostopoulou, Mosharaf Chowdhury +4
Machine learning (ML) models can leak information about users, and differential privacy (DP) provides a rigorous way to bound that leakage under a given budget. This DP budget can…
Holistic Verification of Blockchain Consensus
Nathalie Bertrand, Vincent Gramoli, Igor Konnov +3
Blockchain has recently attracted the attention of the industry due, in part, to its ability to automate asset transfers. It requires distributed participants to reach a consensus…
Privacy Budget Scheduling
Tao Luo, Mingen Pan, Pierre Tholoniat +3
Machine learning (ML) models trained on personal data have been shown to leak information about users. Differential privacy (DP) enables model training with a guaranteed bound on t…