K−Means Clustering Microaggregation for Statistical Disclosure Control
This paper presents a K-means clustering technique that satisfies the bi-objective function to minimize the information loss and maintain k-anonymity. The proposed technique starts with one cluster and subsequently partitions the dataset into two or more clusters such that the total information loss across all clusters is the least, while satisfying the k-anonymity requirement. The structure of K− means clustering problem is defined and investigated and an algorithm of the proposed problem is developed. The performance of the K− means clustering algorithm is compared against the most recent microaggregation methods. Experimental results show that K− means clustering algorithm incurs less information loss than the latest microaggregation methods for all of the test situations.
KeywordsInformation Loss Means Cluster Intra Cluster Distance Statistical Disclosure Control Anonymization Technique
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- 7.Kabir, M.E., Wang, H.: Systematic Clustering-based Microaggregation for Statistical Disclosure Control. In: Proc. IEEE International Conference on Network and System Security, Melbourne, pp. 435–441 (September 2010)Google Scholar
- 8.Kabir, M.E., Wang, H., Bertino, E., Chi, Y.: Systematic Clustering Method for l-diversity Model. In: Proc. Australasian Database Conference, Brisbane, pp. 93–102 (January 2010)Google Scholar
- 9.Kabir, M.E., Wang, H.: Microdata Protection Method Through Microaggragation: A Median Based Approach. Information Security Journal: A Global Perspective (in press)Google Scholar