Sanitization of Call Detail Records via Differentially-Private Bloom Filters

• Published in: Data and Applications Security and Privacy XXIX Vol. 9149; pp. 223 - 230
• Main Authors: Alaggan, Mohammad, Gambs, Sébastien, Matwin, Stan, Tuhin, Mohammed
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2015; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Bloom Filter; Computer security; Differential Privacy; Hash Function; Information architecture; Mean Relative Error; Telecom Operator
• ISBN: 3319208098; 9783319208091
• ISSN: 0302-9743; 1611-3349; 1611-3349
• DOI: 10.1007/978-3-319-20810-7_15

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.