Publications de Jean-Pierre Hubaux

Secure and federated genome-wide association studies for biobank-scale datasets

Sharing data across institutions for genome-wide association studies (GWAS) would enhance the discovery of genetic variation linked to health and disease1,2. However, existing data-sharing regulations limit the scope of such collaborations3. Although crypt ...

NATURE PORTFOLIO2025

VERITAS: Plaintext Encoders for Practical Verifiable Homomorphic Encryption

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

Homomorphic encryption has become a practical 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 ...

Association for Computing Machinery, Inc2024

Scalable and Privacy-Preserving Federated Principal Component Analysis

Jean-Pierre Hubaux, Jean-Philippe Léonard Bossuat, David Jules Froelicher, Joao André Gomes de Sá e Sousa, Juan Ramón Troncoso-Pastoriza, Apostolos Pyrgelis

Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confi ...

IEEE COMPUTER SOC2023

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

An Efficient Threshold Access-Structure for RLWE-Based Multiparty Homomorphic Encryption

Jean-Pierre Hubaux, Christian Vincent Mouchet

We propose and implement a multiparty homomorphic encryption (MHE) scheme with a t-out-of -N-threshold access-structure that is efficient and does not require a trusted dealer in the common random string model. We construct this scheme from the ring-learni ...

SPRINGER2023

slytHErin: An Agile Framework for Encrypted Deep Neural Network Inference

Jean-Pierre Hubaux, Sinem Sav, Apostolos Pyrgelis, Francesco Intoci

Homomorphic encryption (HE), which allows computations on encrypted data, is an enabling technology for confidential cloud computing. One notable example is privacy-preserving Prediction-as-a-Service (PaaS), where machine-learning predictions are computed ...

Springer Nature2023

System and method for privacy-preserving distributed training of neural network models on distributed datasets

Jean-Pierre Hubaux, Jean-Philippe Léonard Bossuat, David Jules Froelicher, Joao André Gomes de Sá e Sousa, Juan Ramón Troncoso-Pastoriza, Sinem Sav, Apostolos Pyrgelis

A computer-implemented method and a distributed computer system (100) for privacy- preserving distributed training of a global neural network model on distributed datasets (DS1 to DSn). The system has a plurality of data providers (DP1 to DPn) being commun ...

2022

Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies

Jean-Pierre Hubaux, Erman Ayday, Juan Ramón Troncoso-Pastoriza, Apostolos Pyrgelis

2022

Privacy-preserving federated neural network learning for disease-associated cell classification

Jean-Pierre Hubaux, Sinem Sav

Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing or centralizing the data of different healthcare institutions is, however, unfeasible or prohibitively difficult due t ...

ELSEVIER2022

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

Bootstrapping for Approximate Homomorphic Encryption with Negligible Failure-Probability by Using Sparse-Secret Encapsulation

Jean-Pierre Hubaux, Jean-Philippe Léonard Bossuat, Juan Ramón Troncoso-Pastoriza

Bootstrapping parameters for the approximate homomorphic-encryption scheme of Cheon et al., CKKS (Asiacrypt 17), are usually instantiated using sparse secrets to be efficient. However, using sparse secrets constrains the range of practical parameters withi ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Every Byte Matters: Traffic Analysis of Bluetooth Wearable Devices

Jean-Pierre Hubaux, Ludovic Barman, Apostolos Pyrgelis

2021

Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation

Jean-Pierre Hubaux, Jean-Philippe Léonard Bossuat, David Jules Froelicher, Juan Ramón Troncoso-Pastoriza, Yiping Ma

Genotype imputation is a fundamental step in genomic data analysis, where missing variant genotypes are predicted using the existing genotypes of nearby "tag"variants. Although researchers can outsource genotype imputation, privacy concerns may prohibit ge ...

CELL PRESS2021

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Jean-Pierre Hubaux, Jean-Philippe Léonard Bossuat, David Jules Froelicher, Joao André Gomes de Sá e Sousa, Juan Ramón Troncoso-Pastoriza, Sinem Sav, Apostolos Pyrgelis

In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an N-party, federated learning setting. We propose a novel system, POSEIDON, the first of its kind in the regime of privacy-preserving neural network ...

INTERNET SOC2021

Revolutionizing Medical Data Sharing Using Advanced Privacy-Enhancing Technologies: Technical, Legal, and Ethical Synthesis

Jean-Pierre Hubaux, Jacques Fellay, Juan Ramón Troncoso-Pastoriza

Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data utility. The shortcomings of traditional privacy-enhancing technologies mean th ...

JMIR PUBLICATIONS, INC2021

Multiparty Homomorphic Encryption from Ring-Learning-with-Errors

Jean-Pierre Hubaux, Jean-Philippe Léonard Bossuat, Christian Vincent Mouchet, Juan Ramón Troncoso-Pastoriza

We propose and evaluate a secure-multiparty-computation (MPC) solution in the semi-honest model with dishonest majority that is based on multiparty homomorphic encryption (MHE). To support our solution, we introduce a multiparty version of the Brakerski-Fa ...

2021

Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption

Jean-Pierre Hubaux, David Jules Froelicher, Jacques Fellay, Jean Louis Raisaro, Joao André Gomes de Sá e Sousa, Juan Ramón Troncoso-Pastoriza

Existing approaches to sharing of distributed medical data either provide only limited protection of patients' privacy or sacrifice the accuracy of results. Here, the authors propose a federated analytics system, based on multiparty homomorphic encryption ...

Nature Portfolio2021

Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-sparse Keys

Jean-Pierre Hubaux, Jean-Philippe Léonard Bossuat, Christian Vincent Mouchet, Juan Ramón Troncoso-Pastoriza

We present a bootstrapping procedure for the full-RNS variant of the approximate homomorphic-encryption scheme of Cheon et al., CKKS (Asiacrypt 17, SAC 18). Compared to the previously proposed procedures (Eurocrypt 18 & 19, CT-RSA 20), our bootstrapping pr ...

SPRINGER INTERNATIONAL PUBLISHING AG2021

Scalable Privacy-Preserving Distributed Learning

Jean-Pierre Hubaux, Jean-Philippe Léonard Bossuat, David Jules Froelicher, Joao André Gomes de Sá e Sousa, Juan Ramón Troncoso-Pastoriza, Sinem Sav, Apostolos Pyrgelis

2021