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Select-organize-anonymize: A framework for trajectory data anonymization

Poulis, Giorgos, Skiadopoulos, Spiros, Loukides, Grigorios ORCID: and Gkoulala-Divanis, Aris 2013. Select-organize-anonymize: A framework for trajectory data anonymization. Presented at: 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW), Dallas, TX, USA, 7-10 December 2013. Published in: Ding, W., Washio, T., Xiong, H., Karypis, G., Thuraisingham, B., Cook, D. and Wu, X. eds. Proceedings of the 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW). Los Alamitos, CA: IEEE, pp. 867-874. 10.1109/ICDMW.2013.136

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Advances in positioning technologies together with the wide adoption of GPS-enabled smartphones enable accurate and low-cost tracking of user location. This allows the collection of large amounts of person-specific mobility data that offer remarkable opportunities for data analysis. Yet, the sharing of such data poses significant privacy risks. This enunciates the need for privacy-preserving, trajectory data publishing methods. Existing approaches are either limited in their privacy specification component or they incur significant, and often unnecessary, data distortion. In response, we propose a novel framework for anonymizing trajectory data that prevents the disclosure of both identity and sensitive location information, while retaining data utility. Our framework involves: (i) selecting similar trajectories, by employing Z-ordering or data projections on frequent sub trajectories, (ii) organizing the selected trajectories into carefully constructed clusters, and (ii) anonymizing each cluster separately. We develop algorithms to realize our framework, which are effective and efficient, as verified by extensive experiments.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: IEEE
ISBN: 9781479931439
Last Modified: 25 Oct 2022 09:38

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