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Data-Aware Privacy-Preserving Machine Learning

Publications associées (84)

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Privacy-Preserving Distributed Collaborative Filtering

Rachid Guerraoui, Anne-Marie Kermarrec, Davide Frey

We propose a new mechanism to preserve privacy while leveraging user profiles in distributed recommender systems. Our mechanism relies on (i) an original obfuscation scheme to hide the exact profiles of users without significantly decreasing their utility, ...

Springer International Publishing2014

C3P: Context-Aware Crowdsourced Cloud Privacy

Karl Aberer, Hamza Harkous, Rameez Rahman

Due to the abundance of attractive services available on the cloud, people are placing an increasing amount of their data online on different cloud platforms. However, given the recent large-scale attacks on users data, privacy has become an important issu ...

Springer-Verlag Berlin2014

Quantifying and Protecting Location Privacy

Reza Shokri

Recent developments in information and communication technologies have been profound and life-changing. Most people are now equipped with smart phones with high computation power and communication capabilities. These devices can efficiently run multiple so ...

EPFL2013

A template privacy protection scheme for fingerprint minutiae descriptors

Leila Mirmohamadsadeghi, Andrzej Drygajlo

It is important in biometric person recognition systems to protect personal data and privacy of users. This paper introduces a new mechanism to revoke and protect fingerprint minutiae information, which can be used in today’s security-aware society. The re ...

2013

MediaEval 2013 Visual Privacy Task: Warping-based Privacy Protection Tool

Touradj Ebrahimi

In this paper, we describe EPFL privacy protection tool submitted to the MediaEval 2013 Visual Privacy task. The goal of the task is to obscure faces and other personal items of people in the provided surveillance clips to preserve their personal privacy. ...

2013

Robust and Private Bayesian Inference

Christos Dimitrakakis, Aikaterini Mitrokotsa

We examine the robustness and privacy properties of Bayesian inference under assumptions on the prior, but without any modifications to the Bayesian framework. First, we generalise the concept of differential privacy to arbitrary dataset distances, outcome ...

2013

Quantifying and Protecting Location Privacy

Reza Shokri

EPFL2012

Privacy Decision Making in the Travel Panopticon

Intelligent Transport Systems (ITS) have the potential to increase road-network capacities, reduce congestion and pollution, create shorter and more predictable journey times and significantly improve road-user safety. However, these technologies will also ...

2012

Adaptive Personalized Privacy in Participatory Sensing

Karl Aberer, Jean-Pierre Hubaux, Rammohan Narendula, Berker Agir

The participatory sensing paradigm has interesting applications, e.g. electrosmog/air-pollution monitoring, carbon footprint estimation, etc., but raises serious privacy concerns. Existing static privacy-enabling approaches offer no privacy guarantees, whi ...

2012

Quantifying Location Privacy

Jean-Yves Le Boudec, Jean-Pierre Hubaux, Reza Shokri, Georgios Theodorakopoulos

It is a well-known fact that the progress of personal communication devices leads to serious concerns about privacy in general, and location privacy in particular. As a response to these issues, a number of Location-Privacy Protection Mechanisms (LPPMs) ha ...

Ieee Computer Soc Press, Customer Service Center, Po Box 3014, 10662 Los Vaqueros Circle, Los Alamitos, Ca 90720-1264 Usa2011