Towards preference-constrained k-anonymisation

Loukides, Grigorios ORCID: https://orcid.org/0000-0003-0888-5061, Tziatzios, Achilleas and Shao, Jianhua ORCID: https://orcid.org/0000-0001-8461-1471 2009. Towards preference-constrained k-anonymisation. Lecture Notes in Computer Science 5667 , pp. 231-245. 10.1007/978-3-642-04205-8_20

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Abstract

In this paper, we propose a novel preference-constrained approach to k-anonymisation. In contrast to the existing works on k-anonymisation which attempt to satisfy a minimum level of protection requirement as a constraint and then optimise data utility within that constraint, we allow data owners and users to specify their detailed protection and usage requirements as a set of preferences on attributes or data values, treat such preferences as constraints and solve them as a multi-objective optimisation problem. This ensures that anonymised data will be actually useful to data users in their applications and sufficiently protected for data owners. Our preliminary experiments show that our method is capable of producing anonymisations that satisfy a range of preferences and have a high level of data utility and protection.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Springer Verlag
ISSN:	0302-9743
Last Modified:	18 Oct 2022 13:32
URI:	https://orca.cardiff.ac.uk/id/eprint/14243

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