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Publications associées (26)

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Differentially private multi-agent constraint optimization

Boi Faltings, Sujit Prakash Gujar, Aleksei Triastcyn, Sankarshan Damle

Distributed constraint optimization (DCOP) is a framework in which multiple agents with private constraints (or preferences) cooperate to achieve a common goal optimally. DCOPs are applicable in several multi-agent coordination/allocation problems, such as ...

Springer2024

Harmonization of multi-scanner in vivo magnetic resonance spectroscopy: ENIGMA consortium task group considerations

Cristina Ramona Cudalbu Moldovan

Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impa ...

FRONTIERS MEDIA SA2023

Toward contactless human thermal monitoring: A framework for Machine Learning-based human thermo-physiology modeling augmented with computer vision

Alexandre Massoud Alahi, Dolaana Khovalyg, Mohamad Rida, Mohamed Ossama Ahmed Abdelfattah

The transition towards a human-centered indoor climate is beneficial from occupants’ thermal comfort and from an energy reduction perspective. However, achieving this goal requires the knowledge of the thermal state of individuals at the level of body part ...

2023

Multi-agent Learning with Privacy Guarantees

Elsa Rizk

A multi-agent system consists of a collection of decision-making or learning agents subjected to streaming observations from some real-world phenomenon. The goal of the system is to solve some global learning or optimization problem in a distributed or dec ...

EPFL2023

Health data privacy through homomorphic encryption and distributed ledger computing: an ethical-legal qualitative expert assessment study

Marcello Ienca

Background: Increasingly, hospitals and research institutes are developing technical solutions for sharing patient data in a privacy preserving manner. Two of these technical solutions are homomorphic encryption and distributed ledger technology. Homomorph ...

BMC2022

Nine recommendations for the governance of AI systems

Marie-Valentine Renée Agnès Florin

Some governance functions traditionally performed by humans are increasingly informed and sometimes automatically executed by machine learning algorithms (governance by machine learning) to benefit society. Therefore, it is necessary to think also about th ...

EPFL2022

Learning from the Ethics of AI-A Research Proposal on Soft Law and Ethics of AI

Johan Robert Rochel

This contribution outlines a research proposal combining ethical guidelines on AI and a law-as-data approach. Building upon the definitions of soft law discussed in legal scholarship, it proposes a way of structuring the regulatory landscape on AI and of a ...

UBIQUITY PRESS LTD2022

Synthetic Data - Anonymisation Groundhog Day

Carmela González Troncoso, Theresa Stadler

Synthetic data has been advertised as a silver-bullet solution to privacy-preserving data publishing that addresses the shortcomings of traditional anonymisation techniques. The promise is that synthetic data drawn from generative models preserves the stat ...

USENIX ASSOC2022

Mental data protection and the GDPR

Marcello Ienca

Although decoding the content of mental states is currently unachievable, technologies such as neural interfaces, affective computing systems, and digital behavioral technologies enable increasingly reliable statistical associations between certain data pa ...

OXFORD UNIV PRESS2022

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

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

Truthful, Transparent and Fair Data Collection Mechanisms

Naman Goel

An important prerequisite for developing trustworthy artificial intelligence is high quality data. Crowdsourcing has emerged as a popular method of data collection in the past few years. However, there is always a concern about the quality of the data thus ...

EPFL2020

MedCo: Enabling Secure and Privacy-Preserving 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

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 ...

2019

MedChain: Accountable and Auditable Data Sharing in Distributed Medical Scenarios

Jean-Pierre Hubaux, Bryan Alexander Ford, Juan Ramón Troncoso-Pastoriza, Jean Louis Raisaro, Linus Gasser

The current trend towards personalized medicine creates an urgent need to share data among different hospitals and health institutions, which endangers the privacy of the data subjects if not done with the appropriate precautions. Conversely, the frequency ...

2019

Privacy-Enhancing Technologies for Mobile Applications and Services

Thi Van Anh Pham

Over a third of the world 's population owns a smartphone. As generic computing devices that support a large and heterogeneous collection of mobile applications (apps), smartphones provide a plethora of functionalities and services to billions of users. B ...

EPFL2019

Interdependent and Multi-Subject Privacy: Threats, Analysis and Protection

Alexandra-Mihaela Olteanu

In Alan Westin's generally accepted definition of privacy, he describes it as an individual's right 'to control, edit, manage, and delete information about them[selves] and decide when, how, and to what extent information is communicated to others.' There ...

EPFL2019

Consensual and Privacy-Preserving Sharing of Multi-Subject and Interdependent Data

Jean-Pierre Hubaux, Kévin Clément Huguenin, Italo Ivan Dacosta Petrocelli, Alexandra-Mihaela Olteanu

Individuals share increasing amounts of personal data online. This data often involves–or at least has privacy implications for–data subjects other than the individual who shares it (e.g., photos, genomic data) and the data is shared without their consent. ...

2018

Privacy-Enhancing Technologies for Medical and Genomic Data: From Theory to Practice

Jean Louis Raisaro

The impressive technological advances in genomic analysis and the significant drop in the cost of genome sequencing are paving the way to a variety of revolutionary applications in modern healthcare. In particular, the increasing understanding of the human ...

EPFL2018

On Secure Cloud Computing for Genomic Data: From Storage to Analysis

Zhicong Huang

Although privacy is generally considered to be the right of an individual or group to control information about themselves, such a right has become challenging to protect in the digital era, this is exemplified by the case of cloud-based genomic computing. ...

EPFL2018