Team, Visitors, External Collaborators
Overall Objectives
Research Program
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Systems for the Support of Privacy

Robust Privacy-Preserving Gossip Averaging

Participants : Amaury Bouchra-Pilet, Davide Frey, François Taïani.

This contribution aims to address the privacy risks inherent in decentralized systems by considering the emblematic problem of privacy-preserving decentralized averaging. In particular, we propose a novel gossip protocol that exchanges noise for several rounds before starting to exchange actual data. This makes it hard for an honest but curious attacker to know whether a user is transmitting noise or actual data. Our protocol and analysis do not assume a lock-step execution, and demonstrate improved resilience to colluding attackers. In a paper, publishing this work at SSS 2019 [26], we prove the correctness of this protocol as well as several privacy results. Finally, we provide simulation results about the efficiency of our averaging protocol.

A Collaborative Strategy for Mitigating Tracking through Browser Fingerprinting.

Participants : David Bromberg, Davide Frey, Alejandro Gomez-Boix.

Browser fingerprinting is a technique that collects information about the browser configuration and the environment in which it is running. This information is so diverse that it can partially or totally identify users online. Over time, several countermeasures have emerged to mitigate tracking through browser fingerprinting. However, these measures do not offer full coverage in terms of privacy protection, as some of them may introduce inconsistencies or unusual behaviors, making these users stand out from the rest.

In this work, we address these limitations by proposing a novel approach that minimizes both the identifiability of users and the required changes to browser configuration. To this end, we exploit clustering algorithms to identify the devices that are prone to share the same or similar fingerprints and to provide them with a new non-unique fingerprint. We then use this fingerprint to automatically assemble and run web browsers through virtualization within a docker container. Thus all the devices in the same cluster will end up running a web browser with an indistinguishable and consistent fingerprint.

We carried out this work in collaboration with Benoit Baudry from KTH Sweden and published our results at the 2019 Moving-Target Defense Workshop [30].