Publications de David Jules Froelicher

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

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

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

Privacy-Preserving Federated Analytics using Multiparty Homomorphic Encryption

David Jules Froelicher

Analyzing and processing data that are siloed and dispersed among multiple distrustful stakeholders is difficult and can even become impossible when the data are sensitive or confidential. Current data-protection and privacy regulations highly restrict the ...

EPFL2021

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

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

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

System and method for privacy-preserving distributed training of machine learning 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 model on distributed datasets (DS1 to DSn). The system has a plurality of data providers (DP1 to DPn) being communicatively coupl ...

2021

MedCo(2): Privacy-Preserving Cohort Exploration and Analysis

Jean-Pierre Hubaux, Mickaël Misbach, David Jules Froelicher, Jean Louis Raisaro, Juan Ramón Troncoso-Pastoriza

Medical studies are usually time consuming, cumbersome and extremely costly to perform, and for exploratory research, their results are also difficult to predict a priori. This is particularly the case for rare diseases, for which finding enough patients i ...

IOS PRESS2020

Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets

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

Data sharing has become of primary importance in many domains such as big-data analytics, economics and medical research, but remains difficult to achieve when the data are sensitive. In fact, sharing personal information requires individuals' unconditiona ...

2020

UnLynx: A Decentralized System for Privacy-Conscious Data Sharing

Jean-Pierre Hubaux, Patricia Egger, David Jules Froelicher, Christian Vincent Mouchet, Zhicong Huang, Jean Louis Raisaro, Bryan Alexander Ford, Joao André Gomes de Sá e Sousa

Current solutions for privacy-preserving data sharing among multiple parties either depend on a centralized authority that must be trusted and provides only weakest-link security (e.g., the entity that manages private/secret cryptographic keys), or leverag ...

De Gruyter2017