Publications de Juan Ramón Troncoso-Pastoriza

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

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

Efficient protocols for oblivious linear function evaluation from ring-LWE

Juan Ramón Troncoso-Pastoriza

An oblivious linear function evaluation protocol, or OLE, is a two-party protocol for the function f (x) = ax + b, where a sender inputs the field elements a, b, and a receiver inputs x and learns f (x). OLE can be used to build secret-shared multiplicatio ...

IOS PRESS2022

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

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

Revisiting Multivariate Ring Learning with Errors and Its Applications on Lattice-Based Cryptography

Juan Ramón Troncoso-Pastoriza

The “Multivariate Ring Learning with Errors” problem was presented as a generalization of Ring Learning with Errors (RLWE), introducing efficiency improvements with respect to the RLWE counterpart thanks to its multivariate structure. Nevertheless, the rec ...

2021

Crypt4GH: a file format standard enabling native access to encrypted data

Juan Ramón Troncoso-Pastoriza

Motivation: The majority of genome analysis tools and pipelines require data to be decrypted for access. This potentially leaves sensitive genetic data exposed, either because the unencrypted data is not removed after analysis, or because the data leaves t ...

OXFORD UNIV PRESS2021

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

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

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

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

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

SCOR: A secure international informatics infrastructure to investigate COVID-19

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza

Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secur ...

OXFORD UNIV PRESS2020

Multiquadratic Rings and Walsh-Hadamard Transforms for Oblivious Linear Function Evaluation

Juan Ramón Troncoso-Pastoriza

The Ring Learning with Errors (RLWE) problem has become one of the most widely used cryptographic assumptions for the construction of modern cryptographic primitives. Most of these solutions make use of power-of-two cyclotomic rings mainly due to its simpl ...

IEEE2020