Abstract
Protecting data privacy is a vital problem in micro data distribution when disclosing individual’s information. k-Anonymization is a popular approach in protecting individuals from being identified when linked to the external data like voters list. A view of k-anonymity is grouping the data values with constraint of minimum number of data points in every group. In this paper we propose Minimum Spanning Tree (MST) approach to achieve k-anonymity. We define hierarchical distance between two tuples as a metric for constructing the edges of MST. Our approach yields an efficient way to achieve k-anonymity and it balances both privacy protection and data utility.
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Venkata Ramana, K., Valli Kumari, V., Raju, K.V.S.V.N. (2012). Minimum Spanning Tree Based k-Anonymity. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 130. Springer, India. https://doi.org/10.1007/978-81-322-0487-9_39
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DOI: https://doi.org/10.1007/978-81-322-0487-9_39
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