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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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Submitted on : Wednesday, March 28, 2018 - 3:58:00 PM
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Mohammad Alaggan, Sébastien Gambs, Stan Matwin, Mohammed Tuhin. Sanitization of Call Detail Records via Differentially-Private Bloom Filters. 29th IFIP Annual Conference on Data and Applications Security and Privacy (DBSEC), Jul 2015, Fairfax, VA, United States. pp.223-230, ⟨10.1007/978-3-319-20810-7_15⟩. ⟨hal-01745827⟩



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