Publications by Sylvain Chatel

Thwarting Malicious Adversaries in Homomorphic Encryption Pipelines

Homomorphic Encryption (HE) enables computations to be executed directly on encrypted data. As such, it is an auspicious solution for protecting the confidentiality of sensitive data without impeding its usability. However, HE does not provide any guarant ...

EPFL2023

PELTA - Shielding Multiparty-FHE against Malicious Adversaries

Jean-Pierre Hubaux, Sylvain Chatel, Christian Vincent Mouchet, Carmela González Troncoso

Multiparty fully homomorphic encryption (MFHE) schemes enable multiple parties to efficiently compute functions on their sensitive data while retaining confidentiality. However, existing MFHE schemes guarantee data confidentiality and the correctness of th ...

Assoc Computing Machinery2023

Poster: Verifiable Encodings for Maliciously-Secure Homomorphic Encryption Evaluation

Jean-Pierre Hubaux, Sylvain Chatel, Christian Louis Knabenhans, Carmela González Troncoso

Homomorphic encryption has become a promising solution for protecting the privacy of computations on sensitive data. However, existing homomorphic encryption pipelines do not guarantee the correctness of the computation result in the presence of a maliciou ...

Assoc Computing Machinery2023

Deploying Decentralized, Privacy-Preserving Proximity Tracing

Jean-Pierre Hubaux, Sylvain Chatel, Edouard Bugnion, James Richard Larus, Marcel Salathé, Carmela González Troncoso, Wouter Lueks, Mathias Josef Payer, Apostolos Pyrgelis, Theresa Stadler

2022

SoK: Privacy-Preserving Collaborative Tree-based Model Learning

Jean-Pierre Hubaux, Sylvain Chatel, Juan Ramón Troncoso-Pastoriza, Apostolos Pyrgelis

2021

Privacy and Integrity Preserving Computations with CRISP

Jean-Pierre Hubaux, Sylvain Chatel, Juan Ramón Troncoso-Pastoriza, Apostolos Pyrgelis

In the digital era, users share their personal data with service providers to obtain some utility, e.g., access to high-quality services. Yet, the induced information flows raise privacy and integrity concerns. Consequently, cautious users may want to prot ...

USENIX ASSOC2021

[Re] Meta learning with differentiable closed-form solvers

Matthias Grossglauser, Sylvain Chatel, Arnout Jan J Devos

In this work, we present a reproduction of the paper of Bertinetto et al. ”Meta- learning with differentiable closed-form solvers” as part of the ICLR 2019 Reproducibility Challenge. In successfully reproducing the most crucial part of the paper, we reach ...

2019