News

Black-box Estimation of the Bayes Risk Using ML Methods

One of the key researches in the domain of quantitative information flow (QIF) is to effectively estimate information leaks in a system in order to prevent adversarial attacks. Most existing approaches are based on the white-box approach. However, this approach is often impractical due to the size or complexity of its internals, or the presence of unknown factors. This and other challenges forced a shift in focus to investigate methods for measuring a system’s leakage in a black-box manner.

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Microchannel Network Inspired by the Human Circularity System

While scientists have successfully reduced the size and costs of electronic components, a major challenge faced by such tiny devices is the absence of an optimum thermal and energy management technology. To bridge that gap, Elison Matioli and his colleagues at EPFL’s Power and Wide-band-gap Electronics Research Laboratory (POWERlab) have developed a novel microchannel network that not only cools electronic components but also makes them energy efficient.

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David Atienza, Babak Falsafi, Marting Jaggi, and Mathias Payer

Facebook-EPFL Joint ML Research Engagement

Facebook and EPFL have initiated a collaborative program that aims to carry out seminal research with common meeting points for both organizations. Facebook seeks to leverage EPFL’s proven expertise in Computer Science and Engineering to enable the flow of technology from one of the most renowned research institutions to the leading American social media conglomerate. The collaboration will also help the latter strengthen its position in Switzerland and gain access to some of the best academic minds in Europe.

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Datashare Network: A Decentralized Search Engine for Journalists

EPFL researchers at the Security and Privacy Engineering (SPRING) Lab, School of Computer and Communication Sciences (IC), have developed a ‘Datashare Network’ that allows investigative journalists to exchange information securely and anonymously. A detailed paper on the subject will be presented by the scientists at the 29th Usenix Security Symposium (USENIX Security ’20), which will be held online from August 12 to 14. The event, which brings together specialists in the security and privacy of computer systems and networks, will undoubtedly draw worldwide attention to the EPFL research.

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EPFL Authors Win ISVLSI 2020 Best Paper Award

The paper “Enabling Optimal Power Generation of Flow Cell Arrays in 3D MPSoCs with On-Chip Switched Capacitor Converters” is a collaborative research by Halima Najibi, Alexandre Levisse, Marina Zapater, and David Atienza, who are associated with EPFL’s Embedded Systems Laboratory (ESL). Considering the reputation of the symposium, built over a period of three decades, it is no mean achievement to have a paper accepted and then selected as the Best Paper. Many congratulations to the EPFL authors for their singular academic triumph.

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Google-Apple–based “SwissCovid” App in Pilot Test

The first coronavirus digital contact-tracing app using OS updates from Google and Apple is now in a large-scale pilot test. Dubbed as SwissCovid, the app is based on the decentralized protocol, where operations that have data privacy implications are not stored or conducted through a centralized server, but on the phone of individual users. Employees at EPFL, ETH Zurich, the Swiss Army, and select hospitals and cantonal administrations can now download the app for tracing contacts at risk of transmission of COVID-19.

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EPFL Field-tests Contact Tracing App

As the name implies, the international Decentralized Privacy-Preserving Proximity Tracing project (DP3T) focuses on data privacy and negates any chance of data being misused by hackers.

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