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Juan Ramón Troncoso-Pastoriza

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Related publications (33)

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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

Data protection and ethics requirements for multisite research with health data: a comparative examination of legislative governance frameworks and the role of data protection technologies

Sotiria Kechagia, Jean-Pierre Hubaux, Jacques Fellay, Juan Ramón Troncoso-Pastoriza, Jean Louis Raisaro

Personalised medicine can improve both public and individual health by providing targeted preventative and therapeutic healthcare. However, patient health data must be shared between institutions and across jurisdictions for the benefits of personalised me ...

OXFORD UNIV PRESS2020

Privacy and Integrity Preserving Computations with CRISP

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

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

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

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

Scalable and Privacy-Preserving Federated Principal Component Analysis

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

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

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

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

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

Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation

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

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

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

Revisiting Multivariate Lattices for Encrypted Signal Processing

Juan Ramón Troncoso-Pastoriza

Multimedia contents are inherently sensitive signals that must be protected when processed in untrusted environments. The field of Secure Signal Processing addresses this challenge by developing methods which enable operating with sensitive signals in a pr ...

ASSOC COMPUTING MACHINERY2019

On Enforcing the Digital Immunity of a Large Humanitarian Organization

Jean-Pierre Hubaux, Bryan Alexander Ford, Juan Ramón Troncoso-Pastoriza, Philipp Svetolik Jovanovic

Humanitarian action, the process of aiding individuals in situations of crises, poses unique information-security challenges due to natural or manmade disasters, the adverse environments in which it takes place, and the scale and multidisciplinary nature o ...

2018

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

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, Francesco Marino

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

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

Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-sparse Keys

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

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

SPHN/PHRT - MedCo in Action: Empowering the Swiss Molecular Tumor Board with Privacy-Preserving and Real-Time Patient Discovery

Jean-Pierre Hubaux, Jacques Fellay, Juan Ramón Troncoso-Pastoriza, Joao André Gomes de Sá e Sousa, Mickaël Misbach, Francesco Marino

MedCo is the first operational system that makes sensitive medical-data available for research in a simple, privacy-conscious and secure way. It enables a consortium of clinical sites to collectively protect their data and to securely share them with inves ...

IOS PRESS2020

Lattigo: A multiparty homomorphic encryption library in Go

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

We present and demo of Lattigo, a multiparty homomorphic encryption library in Go. After a brief introduction of the origin and history of the library, we dive into the most relevant technical aspects that differentiate Lattigo from other existing librarie ...

2020

Multiparty Homomorphic Encryption from Ring-Learning-with-Errors

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

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

ORide: A Privacy-Preserving yet Accountable Ride-Hailing Service

Guillaume Francois Maurice Endignoux, Jean-Pierre Hubaux, Kévin Clément Huguenin, Juan Ramón Troncoso-Pastoriza, Italo Ivan Dacosta Petrocelli, Thi Van Anh Pham

In recent years, ride-hailing services (RHSs) have become increasingly popular, serving millions of users per day. Such systems, however, raise significant privacy concerns, because service providers are able to track the precise mobility patterns of all r ...

2017

MedCo: Enabling Privacy-Conscious Exploration of Distributed Clinical and Genomic Data

Jean-Pierre Hubaux, Bryan Alexander Ford, Juan Ramón Troncoso-Pastoriza, Jean Louis Raisaro, Joao André Gomes de Sá e Sousa, Mickaël Misbach

Being able to share large amounts of sensitive clinical and genomic data across several institutions is crucial for precision medicine to scale up. Unfor- tunately, existing solutions only partially address this challenge and are still unable to provide th ...

2017

Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption

Juan Ramón Troncoso-Pastoriza

Background Privacy-preserving computations on genomic data, and more generally on medical data, is a critical path technology for innovative, life-saving research to positively and equally impact the global population. It enables medical research algorithm ...

2020