An efficient clustering algorithm for k-anonymisation

Loukides, Grigorios ORCID: https://orcid.org/0000-0003-0888-5061 and Shao, Jianhua ORCID: https://orcid.org/0000-0001-8461-1471 2008. An efficient clustering algorithm for k-anonymisation. Journal of Computer Science and Technology 23 (2) , pp. 188-202. 10.1007/s11390-008-9121-3

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Abstract

K-anonymisation is an approach to protecting individuals from being identified from data. Good k-anonymisations should retain data utility and preserve privacy, but few methods have considered these two con°icting requirements together. In this paper, we extend our previous work on a clustering-based method for balancing data utility and privacy protection, and propose a set of heuristics to improve its effectiveness. We introduce new clustering criteria that treat utility and privacy on equal terms and propose sampling-based techniques to optimally set up its parameters. Extensive experiments show that the extended method achieves good accuracy in query answering and is able to prevent linking attacks effectively.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords:	k-anonymisation - data privacy - greedy clustering
Publisher:	Springer
ISSN:	1000-9000
Last Modified:	18 Oct 2022 13:32
URI:	https://orca.cardiff.ac.uk/id/eprint/14242

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