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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.
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. show that the extended method achieves good accuracy in query answering and is able to prevent linking attacks effectively.