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

Loukides, Grigorios and Shao, Jianhua 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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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: 12 Jun 2019 02:51

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