Anonymizing transaction data to eliminate sensitive inferences

Loukides, Grigorios ORCID: https://orcid.org/0000-0003-0888-5061, Gkoulalas-Divanis, Aris and Shao, Jianhua ORCID: https://orcid.org/0000-0001-8461-1471 2010. Anonymizing transaction data to eliminate sensitive inferences. Presented at: DEXA 2010: International Conference on Database and Expert Systems Applications, Bilbao, Spain, 30 August - 3 September 2010. Published in: Garcia Bringas, Pablo, Hameurlain, Abdelkader and Quirchmayr, Gerald eds. Database and Expert Systems Applications: 21st International Conference, DEXA 2010, Bilbao, Spain, August 30 - September 3, 2010, Proceedings, Part I. Lecture Notes in Computer Science. , vol.6261 Springer Verlag, pp. 400-415. 10.1007/978-3-642-15364-8_34

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Abstract

Publishing transaction data containing individuals’ activities may risk privacy breaches, so the need for anonymizing such data before their release is increasingly recognized by organizations. Several approaches have been proposed recently to deal with this issue, but they are still inadequate for preserving both data utility and privacy. Some incur unnecessary information loss in order to protect data, while others allow sensitive inferences to be made on anonymized data. In this paper, we propose a novel approach that enhances both data utility and privacy protection in transaction data anonymization. We model potential inferences of individuals’ identities and their associated sensitive transaction information as a set of implications, and we design an effective algorithm that is capable of anonymizing data to prevent these sensitive inferences with minimal data utility loss. Experiments using real-world data show that our approach outperforms the state-of-the-art method in terms of preserving both privacy and data utility.

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:	Springer Verlag
ISBN:	9783642153631
ISSN:	0302-9743
Last Modified:	18 Oct 2022 13:30
URI:	https://orca.cardiff.ac.uk/id/eprint/14156

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