Abstract
K-anonymity is the most widely used technology in the field of privacy preservation. It has a good performance particularly in protecting data privacy in the scenarios of data publication, location-based service and social network. In this paper, we propose a new algorithm to achieve k-anonymity in a better way through improved clustering, and we optimize the clustering process by considering the overall distribution of quasi-identifier groups in a multidimensional space. With the local optimal clustering, we try our best to guarantee minimized intra-cluster distances and maximized inter-cluster distances. Therefore, the quality of anonymized data can be greatly improved. Compared with some popular algorithms like k-member, Mondrian, and one-time k-means, the experimental results show our algorithm can effectively reduce the information loss while generating equivalence classes. The total information loss of the anonymized dataset decreases by about 20% on average than that of other algorithms. It also performs well in dealing with both numerical attributes and categorical attributes.
This project is partly supported the National Natural Science Foundation of China (No. 61772291), the Natural Science Foundation of Tianjin (No. 17JCZDJC30500), the Open Project Foundation of Information Security Evaluation Center of Civil Aviation, Civil Aviation University of China (No. CAAC-ISECCA-201702).
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Zheng, W., Wang, Z., Lv, T., Ma, Y., Jia, C. (2018). K-Anonymity Algorithm Based on Improved Clustering. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11335. Springer, Cham. https://doi.org/10.1007/978-3-030-05054-2_36
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DOI: https://doi.org/10.1007/978-3-030-05054-2_36
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