An Efficient Clustering Algorithm for k-Anonymisation |
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Authors: | Grigorios Loukides Jian-Hua Shao |
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Affiliation: | (1) School of Computer Science, Cardiff University, Cardiff, U.K. |
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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 conflicting 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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Keywords: | k-anonymisation data privacy greedy clustering |
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