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An Efficient Clustering Algorithm for k-Anonymisation

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Abstract

K-anonymisation is an approach to protecting individuals from being identified from data. Good k-anonymisations should retain data utility and preserve privacy, but few methods have considered these two con°icting requirements together. In this paper, we extend our previous work on a clustering-based method for balancing data utility and privacy protection, and propose a set of heuristics to improve its effectiveness. We introduce new clustering criteria that treat utility and privacy on equal terms and propose sampling-based techniques to optimally set up its parameters. Extensive experiments show that the extended method achieves good accuracy in query answering and is able to prevent linking attacks effectively.

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  1. School of Computer Science, Cardiff University, Cardiff, U.K.

    Grigorios Loukides & Jian-Hua Shao

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  1. Grigorios Loukides
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  2. Jian-Hua Shao
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Correspondence to Grigorios Loukides.

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Loukides, G., Shao, JH. An Efficient Clustering Algorithm for k-Anonymisation. J. Comput. Sci. Technol. 23, 188–202 (2008). https://doi.org/10.1007/s11390-008-9121-3

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  • DOI: https://doi.org/10.1007/s11390-008-9121-3

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