Distance-based k^m-anonymization of trajectory data

Poulis, Giorgos, Skiadopoulos, Spiros, Loukides, Grigorios ORCID: https://orcid.org/0000-0003-0888-5061 and Gkoulalas-Divanis, Aris 2013. Distance-based k^m-anonymization of trajectory data. Presented at: 2013 IEEE 14th International Conference on Mobile Data Management (MDM), Milan, Italy, 3-6 June 2013. Published in: Bettini, C. and Wolfson, O. eds. Proceedings of the 2013 IEEE 14th International Conference on Mobile Data Management (MDM). , vol.2 Los Alamitos, CA: IEEE, pp. 57-62. 10.1109/MDM.2013.66

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Abstract

The publication of trajectory data opens up new directions in studying human behavior, but it is challenging to perform in a privacy-preserving way. This is mainly because, the identities of individuals, whose movement is recorded in the data, can be disclosed, even after removing identifying information. Existing works to anonymize trajectory data offer privacy, but at a high data utility cost. This is because, they either do not produce truthful data, which is important in many applications, or are limited in their privacy specification component. This paper proposes an approach that overcomes these shortcomings by adapting km-anonymity to trajectory data and by using distance-based generalization. We also develop an effective and efficient anonymization algorithm, which is based on the apriori principle. Our experiments verify that this algorithm preserves data utility well, and it is fast and scalable.

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:	9781467360685
Last Modified:	25 Oct 2022 09:38
URI:	https://orca.cardiff.ac.uk/id/eprint/59437

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