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SwapMob: Swapping trajectories for mobility anonymization
Internet Interdisciplinary Institute (IN3), CYBERCAT-Center for Cybersecurity Research of Catalonia, Universitat Oberta de Catalunya (UOC), Barcelona, Spain.
Internet Interdisciplinary Institute (IN3), CYBERCAT-Center for Cybersecurity Research of Catalonia, Universitat Oberta de Catalunya (UOC), Barcelona, Spain.
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Skövde Artificial Intelligence Lab (SAIL))ORCID-id: 0000-0002-0368-8037
2018 (engelsk)Inngår i: Privacy in Statistical Databases: UNESCO Chair in Data Privacy, International Conference, PSD 2018, Valencia, Spain, September 26–28, 2018, Proceedings / [ed] Josep Domingo-Ferrer, Fransisco Montes, Springer, 2018, s. 331-346Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Mobility data mining can improve decision making, from planning transports in metropolitan areas to localizing services in towns. However, unrestricted access to such data may reveal sensible locations and pose safety risks if the data is associated to a specific moving individual. This is one of the many reasons to consider trajectory anonymization. Some anonymization methods rely on grouping individual registers on a database and publishing summaries in such a way that individual information is protected inside the group. Other approaches consist of adding noise, such as differential privacy, in a way that the presence of an individual cannot be inferred from the data. In this paper, we present a perturbative anonymization method based on swapping segments for trajectory data (SwapMob). It preserves the aggregate information of the spatial database and at the same time, provides anonymity to the individuals. We have performed tests on a set of GPS trajectories of 10,357 taxis during the period of Feb. 2 to Feb. 8, 2008, within Beijing. We show that home addresses and POIs of specific individuals cannot be inferred after anonymizing them with SwapMob, and remark that the aggregate mobility data is preserved without changes, such as the average length of trajectories or the number of cars and their directions on any given zone at a specific time.

sted, utgiver, år, opplag, sider
Springer, 2018. s. 331-346
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11126
Emneord [en]
aggregates, data mining, database systems, decision making, trajectories, anonymization, average length, differential privacies, gps trajectories, metropolitan area, mobility datum, spatial database, trajectory data, data privacy
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science; INF303 Informationssäkerhet
Identifikatorer
URN: urn:nbn:se:his:diva-16303DOI: 10.1007/978-3-319-99771-1_22Scopus ID: 2-s2.0-85053904138ISBN: 978-3-319-99770-4 (tryckt)ISBN: 978-3-319-99771-1 (digital)OAI: oai:DiVA.org:his-16303DiVA, id: diva2:1256117
Konferanse
International Conference on Privacy in Statistical Databases, PSD 2018, Valencia, Spain, September 26–28, 2018
Tilgjengelig fra: 2018-10-16 Laget: 2018-10-16 Sist oppdatert: 2019-02-14bibliografisk kontrollert

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