Search Results for author: Jean-Pierre Hubaux

Found 5 papers, 2 papers with code

Orchestrating Collaborative Cybersecurity: A Secure Framework for Distributed Privacy-Preserving Threat Intelligence Sharing

no code implementations6 Sep 2022 Juan R. Trocoso-Pastoriza, Alain Mermoud, Romain Bouyé, Francesco Marino, Jean-Philippe Bossuat, Vincent Lenders, Jean-Pierre Hubaux

However, this activity presents challenges due to the tension between data sharing and confidentiality, that result in information retention often leading to a free-rider problem.

Privacy Preserving

SoK: Privacy-Preserving Collaborative Tree-based Model Learning

no code implementations16 Mar 2021 Sylvain Chatel, Apostolos Pyrgelis, Juan Ramon Troncoso-Pastoriza, Jean-Pierre Hubaux

Tree-based models are among the most efficient machine learning techniques for data mining nowadays due to their accuracy, interpretability, and simplicity.

BIG-bench Machine Learning Privacy Preserving

POSEIDON: Privacy-Preserving Federated Neural Network Learning

no code implementations1 Sep 2020 Sinem Sav, Apostolos Pyrgelis, Juan R. Troncoso-Pastoriza, David Froelicher, Jean-Philippe Bossuat, Joao Sa Sousa, Jean-Pierre Hubaux

In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an $N$-party, federated learning setting.

Federated Learning Privacy Preserving

Privacy and Integrity Preserving Computations with CRISP

1 code implementation8 Jul 2020 Sylvain Chatel, Apostolos Pyrgelis, Juan R. Troncoso-Pastoriza, Jean-Pierre Hubaux

Service providers are interested in verifying the integrity of the users' data to improve their services and obtain useful knowledge for their business.

Cryptography and Security

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