Abstract
Privacy preservation is a serious concern of today’s scenario. Anonymity is a way to preserve privacy of different types of data publications like structural and descriptive. In this paper, we discuss the preservation of both types of data. We use anatomy for descriptive data and sequential clustering for structural data anonymization. The proposed approach is based on assortativity called Anatomy Based Modified Sequential Clustering (ABMSC). We propose a new total info. loss measure for cluster optimization. The performance of the proposed approach is measured by information loss and number of node movements takes place to meet the optimization. The experimental results show that our proposed algorithm outperforms the two state-of-the-art algorithms.
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Mohapatra, D., Patra, M.R. (2020). Cluster-Based Anonymization of Assortative Networks. In: Behera, H., Nayak, J., Naik, B., Pelusi, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 990. Springer, Singapore. https://doi.org/10.1007/978-981-13-8676-3_60
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DOI: https://doi.org/10.1007/978-981-13-8676-3_60
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