Data utility and privacy protection trade-off in k-anonymisation

Abstract

K-anonymisation is an approach to protecting privacy contained within a dataset. A good k-anonymisation algorithm should anonymise a dataset in such a way that private information contained within it is hidden, yet the anonymised data is still useful in intended applications. However, maximising both data utility and privacy protection in k-anonymisation is not possible. Existing methods derive k-anonymisations by trying to maximise utility while satisfying a required level of protection. In this paper, we propose a method that attempts to optimise the trade-off between utility and protection. We introduce a measure that captures both utility and protection, and an algorithm that exploits this measure using a combination of clustering and partitioning techniques. Our experiments show that the proposed method is capable of producing k-anonymisations with required utility and protection trade-off and with a performance scalable to large datasets.