Juan Ramón Troncoso-Pastoriza

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

One major obstacle to developing precision medicine to its full potential is the privacy concerns related to genomic-data sharing. Even though the academic community has proposed many solutions to protect genomic privacy, these so far have not been adopted ...

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 ...

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 ...

The increasing number of health-data breaches is creating a complicated environment for medical-data sharing and, consequently, for medical progress. Therefore, the development of new solutions that can reassure clinical sites by enabling privacy-preservin ...

Juan Ramón Troncoso-Pastoriza

Chat with Graph Search

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

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

Privacy and Integrity Preserving Computations with CRISP

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

Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation

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

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Revisiting Multivariate Lattices for Encrypted Signal Processing

On Enforcing the Digital Immunity of a Large Humanitarian Organization

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

Efficient protocols for oblivious linear function evaluation from ring-LWE

Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-sparse Keys

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

Lattigo: A multiparty homomorphic encryption library in Go

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

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

GenoShare: Supporting Privacy-Informed Decisions for Sharing Individual-Level Genetic Data

System and method for privacy-preserving distributed training of machine learning models on distributed datasets

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

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