Publications associées à Droit à l'oubli

Bayesian Differential Privacy for Machine Learning

%0 Conference Paper %T Bayesian Differential Privacy for Machine Learning %A Aleksei Triastcyn %A Boi Faltings %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E A ...

2020

Subdomain and Access Pattern Privacy Trading off Confidentiality and Performance

Bin Lu, Thomas Locher, Matus Harvan

Homomorphic encryption and secure multi-party computation enable computations on encrypted data. However, both techniques suffer from a large performance overhead. While advances in algorithms might reduce the overhead, we show that achieving perfect (or e ...

Scitepress2016

Right-Protected Data Publishing with Provable Distance-Based Mining

Michail Vlachos, Nikolaos Freris

Protection of one's intellectual property is a topic with important technological and legal facets. We provide mechanisms for establishing the ownership of a dataset consisting of multiple objects. The algorithms also preserve important properties of the d ...

Ieee Computer Soc2014

Trust-aware peer sampling: Performance and privacy tradeoffs

Anne-Marie Kermarrec, Davide Frey, Julien Michel Stainer

The ability to identify people that share one's own interests is one of the most interesting promises of the Web 2.0 driving user-centric applications such as recommendation systems or collaborative marketplaces. To be truly useful, however, information ab ...

Elsevier Science Bv2013

A Note on a Privacy-Preserving Distance-Bounding Protocol

Aikaterini Mitrokotsa, Jean-Philippe Aumasson

Distance bounding protocols enable a device to establish an upper bound on the physical distance to a communication partner so as to prevent location spoofing, as exploited by relay attacks. Recently, Rasmussen and Cˇapkun (ACM-CCS’08) observed that these ...

Springer2011