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Quantifying Interdependent Privacy Risks with Location Data

Publications associées (42)

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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

On (The Lack Of) Location Privacy in Crowdsourcing Applications

Mathias Jacques Jean-Marc Humbert, Carmela González Troncoso

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

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

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

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

Data-Driven, Personalized Usable Privacy

Hamza Harkous

We live in the "inverse-privacy" world, where service providers derive insights from users' data that the users do not even know about. This has been fueled by the advancements in machine learning technologies, which allowed providers to go beyond the supe ...

EPFL2017

Networks on Chips: 15 Years Later

Giovanni De Micheli, Luca Benini

The authors of a Computer article from 2002 reflect on their proposal to use networks on chips to address scalable communications on silicon VLSI chips. ...

2017

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Mobile users increasingly make use of location-based online services enabled by localization systems. Not only do they share their locations to obtain contextual services in return (e.g., 'nearest restaurant'), but they also share, with their friends, info ...

2016

With ever-increasing computational power, and improved sensing and communication capabilities, smart devices have altered and enhanced the way we process, perceive and interact with information. Personal and contextual data is tracked and stored extensivel ...

EPFL2016

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Contextual information about users is increasingly shared on mobile social networks. Examples of such information include users' locations, events, activities, and the co-presence of others in proximity. When disclosing personal information, users take int ...

Elsevier Science Bv2016