Capturing data usefulness and privacy protection in K-anonymisation

Loukidis, Grigorios and Shao, Jianhua ORCID: https://orcid.org/0000-0001-8461-1471 2007. Capturing data usefulness and privacy protection in K-anonymisation. Presented at: Proceedings of the 2007 ACM Symposium on Applied Computing, Seoul, South Korea, 11-15 March 2007. SAC '07 Proceedings of the 2007 ACM Symposium on Applied Computing. New York, NY: ACM, pp. 370-374. 10.1145/1244002.1244091

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Abstract

K-anonymisation is an approach to protecting privacy contained within a data set. A good k-anonymisation algorithm should anonymise a data set in such a way that private information contained within it is hidden, yet anonymised data is still useful in intended applications. Maximising both data usefulness and privacy protection in k-anonymisation is however difficult. In this paper, we suggest a metric that attempts to quantify these two properties and introduce a clustering based algorithm that can achieve a balance between them in k-anonymisation.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics Schools > Lifelong Learning
Publisher:	ACM
ISBN:	9781595934802
Last Modified:	26 Nov 2025 15:00
URI:	https://orca.cardiff.ac.uk/id/eprint/1883

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