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Capturing data usefulness and privacy protection in K-anonymisation

Loukides, Grigorios and Shao, Jianhua 2007. Capturing data usefulness and privacy protection in K-anonymisation. Presented at: 22nd Annual ACM Symposium on Applied Computing, Seoul, 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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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: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: ACM
ISBN: 9781595934802
Last Modified: 12 Jun 2019 02:52

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