Publications by Mathias Jacques Jean-Marc Humbert

The (Co)-Location Sharing Game

Jean-Pierre Hubaux, Mathias Jacques Jean-Marc Humbert, Kévin Clément Huguenin, Alexandra-Mihaela Olteanu

Most popular location-based social networks, such as Facebook and Foursquare, let their (mobile) users post location and co-location (involving other users) information. Such posts bring social benefits to the users who post them but also to their friends ...

2019

A Survey on Interdependent Privacy

Kévin Clément Huguenin, Mathias Jacques Jean-Marc Humbert, Benjamin Trubert

The privacy of individuals does not only depend on their own actions and data but may also be affected by the privacy decisions and by the data shared by others individuals. This interdependence is an essential aspect of privacy and ignoring it can lead to ...

2019

ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Mathias Jacques Jean-Marc Humbert

Machine learning (ML) has become a core component of many real-world applications and training data is a key factor that drives current progress. This huge success has led Internet companies to deploy machine learning as a service (MLaaS). Recently, the fi ...

The Internet Society2019

On (The Lack Of) Location Privacy in Crowdsourcing Applications

Carmela González Troncoso, Mathias Jacques Jean-Marc Humbert

Crowdsourcing enables application developers to benefit from large and diverse datasets at a low cost. Specifically, mobile crowdsourcing (MCS) leverages users' devices as sensors to perform geo-located data collection. The collection of geo-located data r ...

USENIX ASSOC2019

Simulating the Large-Scale Erosion of Genomic Privacy Over Time

Mathias Jacques Jean-Marc Humbert, Verena Wolf, Xiaoyu Shen

The dramatically decreasing costs of DNA sequencing have triggered more than a million humans to have their genotypes sequenced. Moreover, these individuals increasingly make their genomic data publicly available, thereby creating privacy threats for thems ...

IEEE COMPUTER SOC2018

GenoShare: Supporting Privacy-Informed Decisions for Sharing Exact Genomic Data

Jean-Pierre Hubaux, Jean Louis Raisaro, Carmela González Troncoso, Mathias Jacques Jean-Marc Humbert, Amalio Telenti

The academic community has proposed many solutions to address the privacy concerns associated with genomic-data sharing. However, practitioners have not adopted these solutions due to their impact on the data utility. To address this problem, we introduce ...

EPFL infoscience2018

Towards Usable Checksums: Automating the Integrity Verification ofWeb Downloads for the Masses

Kévin Clément Huguenin, Igor Bilogrevic, Mathias Jacques Jean-Marc Humbert, Mauro Cherubini, Bertil Chapuis, Alexandre Meylan

Internet users can download software for their computers from app stores (e.g., Mac App Store and Windows Store) or from other sources, such as the developers' websites. Most Internet users in the US rely on the latter, according to our representative stud ...

ASSOC COMPUTING MACHINERY2018

Tagvisor: A Privacy Advisor for Sharing Hashtags

Mathias Jacques Jean-Marc Humbert

Hashtag has emerged as a widely used concept of popular culture and campaigns, but its implications on people's privacy have not been investigated so far. In this paper, we present the first systematic analysis of privacy issues induced by hashtags. We con ...

ASSOC COMPUTING MACHINERY2018

Quantifying Interdependent Privacy Risks with Location Data

Jean-Pierre Hubaux, Reza Shokri, Kévin Clément Huguenin, Mathias Jacques Jean-Marc Humbert, Alexandra-Mihaela Olteanu

Co-location information about users is increasingly available online. For instance, mobile users more and more frequently report their co-locations with other users in the messages and in the pictures they post on social networking websites by tagging the ...

Ieee Computer Soc2017

Privacy-enhancing technologies for medical tests using genomic data

Jean-Pierre Hubaux, Jacques Fellay, Erman Ayday, Jacques Rougemont, Jean Louis Raisaro, Mathias Jacques Jean-Marc Humbert, Amalio Telenti

In this invention, we propose privacy-enhancing technologies for medical tests and personalized medicine methods, which utilize patients' genomic data. Assuming the whole genome sequencing is done by a certified institution, we propose to store patients' g ...

2016