Clustering-based K-anonymisation algorithms

Loukides, Grigorios ORCID: https://orcid.org/0000-0003-0888-5061 and Shao, Jianhua ORCID: https://orcid.org/0000-0001-8461-1471 2007. Clustering-based K-anonymisation algorithms. Lecture Notes in Computer Science 4653 , pp. 761-771. 10.1007/978-3-540-74469-6_74

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Abstract

K-anonymisation is an approach to protecting private information contained within a dataset. Many k-anonymisation methods have been proposed recently and one class of such methods are clustering-based. These methods are able to achieve high quality anonymisations and thus have a great application potential. However, existing clustering-based techniques use different quality measures and employ different data grouping strategies, and their comparative quality and performance are unclear. In this paper, we present and experimentally evaluate a family of clustering-based k-anonymisation algorithms in terms of data utility, privacy protection and processing efficiency.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Additional Information:	Subtitle: 18th International Conference, DEXA 2007, Regensburg, Germany, September 3-7, 2007. Proceedings ISBN: 978-3-540-74467-2
Publisher:	Springer Verlag
ISSN:	0302-9743
Last Modified:	24 Oct 2022 10:05
URI:	https://orca.cardiff.ac.uk/id/eprint/43087

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