An efficient privacy-preserving approach for data publishing

Privacy-preserving algorithm based on k-anonymity plays an outstanding role in real-world data mining applications, such as medical records, bioinformatics, market, and social network. How to maximize the availability of published data without sacrificing users’ privacy is the emphasis of privacy-preserving research. In this paper, we propose a mixed-feature weighted clustering algorithm for k-anonymity (MWCK) to study the contradiction of efficiency and information loss for utility-type anonymization. First, we propose the concept of natural equivalence group, then tuples with same attributes in dataset can be pre-extracted to reduce time complexity and information loss. Second, a sorting algorithm based on the shortest distance is proposed, which selects the optimal initial cluster center at a lower computational cost to reduce the number of iterations. Finally, MWCK not only considers intra-cluster isomorphism to reduce generalization information loss and inter-cluster heterogeneity to avoid local optimal solutions, but also applies to both numerical and categorical datasets. Extensive experiments show that our algorithm can effectively protect data privacy and has better comprehensive performance in terms of information loss and computational complexity than state-of-art methods.