Sanitization of Call Detail Records via Differentially-Private Bloom Filters

Abstract : Publishing directly human mobility data raises serious privacy issues due to its inference potential, such as the (re-)identification of individuals. To address these issues and to foster the development of such applications in a privacy-preserving manner, we propose in this paper a novel approach in which Call Detail Records (CDRs) are summarized under the form of a differentially-private Bloom filter for the purpose of privately estimating the number of mobile service users moving from one area (region) to another in a given time frame. Our sanitization method is both time and space efficient, and ensures differential privacy while solving the shortcomings of a solution recently proposed. We also report on experiments conducted using a real life CDRs dataset, which show that our method maintains a high utility while providing strong privacy.
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Communication dans un congrès
Pierangela Samarati. 29th IFIP Annual Conference on Data and Applications Security and Privacy (DBSEC), Jul 2015, Fairfax, VA, United States. Springer International Publishing, Lecture Notes in Computer Science, LNCS-9149, pp.223-230, 2015, Data and Applications Security and Privacy XXIX. 〈10.1007/978-3-319-20810-7_15 〉
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Mohammad Alaggan, Sébastien Gambs, Stan Matwin, Mohammed Tuhin. Sanitization of Call Detail Records via Differentially-Private Bloom Filters. Pierangela Samarati. 29th IFIP Annual Conference on Data and Applications Security and Privacy (DBSEC), Jul 2015, Fairfax, VA, United States. Springer International Publishing, Lecture Notes in Computer Science, LNCS-9149, pp.223-230, 2015, Data and Applications Security and Privacy XXIX. 〈10.1007/978-3-319-20810-7_15 〉. 〈hal-01745827〉

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