C3P: Context-Aware Crowdsourced Cloud Privacy

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Publications associées (30)

Surrogate modelling for seismic collapse risk assessment of steel moment resisting frames

Dimitrios Lignos, Andronikos Skiadopoulos, Nenad Bijelic

With the overall goal of reducing the numerical burden of uncertainty quantification involved in seismic risk assessments, this paper examines opportunities for assessment of seismic collapse capacity and risk using data-driven surrogates as a complement t ...

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

Numerical simulation of the fate, transport and public health risks of enteric viruses in Lake Geneva

Chaojie Li

Lakes are a fundamental feature of nature with brilliance, profoundness and complexity. Various of physical, chemical and biological changes take place three dimensionally in deep lakes, regulated by complicated boundary conditions. To understand and predi ...

EPFL2023

Combining European and US risk prediction models with polygenic risk scores to refine cardiovascular prevention: the CoLaus|PsyCoLaus Study

Stéphane Fournier, Roxane De La Harpe, Jacques Fellay, Christian Axel Wandall Thorball

Lay Summary The aim of this study is to determine whether using polygenic risk scores improves the prediction of atherosclerotic cardiovascular disease risk when combined with clinical scores currently recommended by European and US guidelines on cardiovas ...

OXFORD UNIV PRESS2023

Enforcing Privacy in Distributed Learning With Performance Guarantees

Ali H. Sayed, Stefan Vlaski, Elsa Rizk

We study the privatization of distributed learning and optimization strategies. We focus on differential privacy schemes and study their effect on performance. We show that the popular additive random perturbation scheme degrades performance because it is ...

Ieee-Inst Electrical Electronics Engineers Inc2023

On the (In)security of Peer-to-Peer Decentralized Machine Learning

Mathilde Aliénor Raynal, Carmela González Troncoso, Dario Pasquini

In this work, we carry out the first, in-depth, privacy analysis of Decentralized Learning-a collaborative machine learning framework aimed at addressing the main limitations of federated learning. We introduce a suite of novel attacks for both passive and ...

IEEE COMPUTER SOC2023

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Rachid Guerraoui, John Stephan, Nirupam Gupta, Rafaël Benjamin Pinot, Youssef Allouah

The ubiquity of distributed machine learning (ML) in sensitive public domain applications calls for algorithms that protect data privacy, while being robust to faults and adversarial behaviors. Although privacy and robustness have been extensively studied ...

2023

Not Yet Another Digital ID: Privacy-Preserving Humanitarian Aid Distribution

Justinas Sukaitis, Carmela González Troncoso, Wouter Lueks, Boya Wang

Humanitarian aid-distribution programs help bring physical goods to people in need. Traditional paper-based solutions to support aid distribution do not scale to large populations and are hard to secure. Existing digital solutions solve these issues, at th ...

IEEE COMPUTER SOC2023

Efficient and Accurate Peer-to-Peer Training of Machine Learning Based Home Thermal Models

Rachid Guerraoui, Omid Ardakanian, Alexandre David Olivier Maurer

The integration of smart thermostats in home automation systems has created an opportunity to optimize space heating and cooling through the use of machine learning, for example for thermal model identification. Nonetheless, its full potential remains unta ...

Assoc Computing Machinery2023

Predicting type 2 diabetes risk before and after solid organ transplantation using polygenic scores in a Danish cohort

Jacques Fellay, Christian Axel Wandall Thorball

Type 2 diabetes mellitus (T2DM) can be multifactorial where both genetics and environmental factors play a role. We aimed to investigate the use of polygenic risk scores (PRS) in the prediction of pre-transplant T2DM and post-transplant diabetes mellitus ( ...

Frontiers Media Sa2023

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