activity
20192024
most citedFeasibility of Cross-Chain Payment with Success Guarantees

10 citations · 19 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CR2024

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…

cs.DB2023★ 2 cited

Turbo: Effective Caching in Differentially-Private Databases

Kelly Kostopoulou, Pierre Tholoniat, Asaf Cidon +2

Differentially-private (DP) databases allow for privacy-preserving analytics over sensitive datasets or data streams. In these systems, user privacy is a limited resource that must…

cs.CR2022★ 2 cited

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…

cs.CR2022

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…

cs.CR2021★ 1 cited

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…

cs.DC2020★ 10 cited

Feasibility of Cross-Chain Payment with Success Guarantees

Rob van Glabbeek, Vincent Gramoli, Pierre Tholoniat

We consider the problem of cross-chain payment whereby customers of different escrows---implemented by a bank or a blockchain smart contract---successfully transfer digital assets…

cs.LG2020★ 1 cited

ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing

Théo Ryffel, Pierre Tholoniat, David Pointcheval +1

We propose AriaNN, a low-interaction privacy-preserving framework for private neural network training and inference on sensitive data. Our semi-honest 2-party computation protocol…

cs.DC2019★ 3 cited

Cross-Chain Payment Protocols with Success Guarantees

Rob van Glabbeek, Vincent Gramoli, Pierre Tholoniat

In this paper, we consider the problem of cross-chain payment whereby customers of different escrows -- implemented by a bank or a blockchain smart contract -- successfully transfe…