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Protecting Location Privacy: Optimal Strategy against Localization Attacks

Publications associées (45)

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Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...

2023

Protecting privacy through metadata analysis

Sandra Deepthy Siby

Although encryption hides the content of communications from third parties, metadata, i.e., the information attached to the content (such as the size or timing of communication) can be a rich source of details and context. In this dissertation, we demonstr ...

EPFL2022

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

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

Rethinking Location Privacy for Unknown Mobility Behaviors

Carmela González Troncoso, Simón Oya Diez

Location Privacy-Preserving Mechanisms (LPPMs) in the literature largely consider that users' data available for training wholly characterizes their mobility patterns. Thus, they hardwire this information in their designs and evaluate their privacy propert ...

IEEE COMPUTER SOC2019

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Privacy protection is drawing more attention with the advances in image processing, visual and social media. Photo sharing is a popular activity, which also brings the concern of regulating permissions associated with shared content. This paper presents a ...

2019

Generating Artificial Data for Private Deep Learning

Boi Faltings, Aleksei Triastcyn

In this paper, we propose generating artificial data that retain statistical properties of real data as the means of providing privacy for the original dataset. We use generative adversarial networks to draw privacy-preserving artificial data samples and d ...

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

Quantifying Interdependent Privacy Risks with Location Data

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