Show me how you move and I will tell you who you are

Abstract : Due to the emergence of geolocated applications, more and more mobility traces are generated on a daily basis and collected in the form of geolocated datasets. If an unauthorized entity can access this data, it can used it to infer personal information about the individuals whose movements are contained within these datasets, such as learning their home and place of work or even their social network, thus causing a privacy breach. In order to protect the privacy of individuals, a sanitization process, which adds uncertainty to the data and removes some sensible information, has to be performed. The global objective of GEPETO (for GEoPrivacy Enhancing TOolkit) is to provide researchers concerned with geo-privacy with means to evaluate various sanitization techniques and inference attacks on geolocated data. In this paper, we report on our preliminary experiments with GEPETO for comparing different clustering algorithms and heuristics that can be used as inference attacks, and evaluate their efficiency for the identification of point of interests, as well as their resilience to sanitization mechanisms such as sampling and perturbation.
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Communication dans un congrès
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS (SPRINGL'10), Nov 2010, San José, United States. 2010, 〈10.1145/1868470.1868479〉
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https://hal.inria.fr/inria-00556833
Contributeur : Sébastien Gambs <>
Soumis le : lundi 17 janvier 2011 - 19:52:48
Dernière modification le : jeudi 11 janvier 2018 - 06:26:29

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Sébastien Gambs, Marc-Olivier Killijian, Miguel Nuñez del Prado Cortez. Show me how you move and I will tell you who you are. Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS (SPRINGL'10), Nov 2010, San José, United States. 2010, 〈10.1145/1868470.1868479〉. 〈inria-00556833〉

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