Speeding up clustering-based k-anonymisation algorithms with pre-partitioning

Loukides, Grigorios ORCID: https://orcid.org/0000-0003-0888-5061 and Shao, Jianhua ORCID: https://orcid.org/0000-0001-8461-1471 2007. Speeding up clustering-based k-anonymisation algorithms with pre-partitioning. Lecture Notes in Computer Science 4587 , pp. 203-214. 10.1007/978-3-540-73390-4_23

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Abstract

K-anonymisation is a technique for protecting privacy contained within a dataset. Many k-anonymisation algorithms have been proposed, and one class of such algorithms are clustering-based. These algorithms can offer high quality solutions, but are rather inefficient to execute. In this paper, we propose a method that partitions a dataset into groups first and then clusters the data within each group for k-anonymisation. Our experiments show that combining partitioning with clustering can improve the performance of clustering-based k-anonymisation algorithms significantly while maintaining the quality of anonymisations they produce.

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: 24th British National Conference on Databases, BNCOD 24, Glasgow, UK, July 3-5, 2007. Proceedings ISBN: 9783540733898
Publisher:	Springer Berlin Heidelberg
ISSN:	0302-9743
Last Modified:	21 Oct 2022 10:46
URI:	https://orca.cardiff.ac.uk/id/eprint/41294

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