Abstract
Social network data are generally published in the form of social graphs which are being used for extensive scientific research. We have noticed that even a k-degree anonymization of social graph can’t ensure protection against identity disclosure. In this paper, we have discussed how closeness centrality measure can be used to identify a social entity in the presence of kdegree anonymization. We have proposed a new model called k-degree closeness anonymization by adopting a mixed strategy of k-degree anonymity, degree centrality and closeness centrality. The model has two phases, namely, construction and validation. The construction phase transforms a graph with given sequence to a graph with anonymous sequence in such a manner that the closeness centrality measure is distributed among the nodes in a smooth way. The nodes with the same degree centrality are assigned with a closer set of closeness centrality values, making re-identification difficult. Validation phase validates our model by generating 1-neighborhood graphs. Algorithms have been developed both for the construction and validation phases.
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References
Lindell, Y., Pinkas, B.: Secure Multiparty Computation for Privacy-Preserving Data Mining. The Journal of Privacy and Confidentiality 1, 59–98 (2009)
Hann, J., Kamber, N.: Data mining: Concepts and techniques. MorganKanfmann Publishers, San Francisco (2001)
Aggarwal, G., Feder, T., Kenthapadi, K., Motwani, R., Panigrahy, R., Thomas, D., Zhu, A.: Anonymizing tables. In: Eiter, T., Libkin, L. (eds.) ICDT 2005. LNCS, vol. 3363, pp. 246–258. Springer, Heidelberg (2005)
Sweeney, L.: k-ANONYMITY: A Model for Protecting Privacy, International Journal on Uncertainty. Fuzziness and Knowledge-based Systems 10(5), 557–570 (2002)
Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: l-diversity: Privacy beyond k-anonymity. In: Proc. 22nd Intnl. Conf. Data Engg (ICDE), p. 24 (2006)
Chi-Wing Wong, R., Li, J., Fu, A.W., Wang, K.: (α,k)Anonymity: An Enhanced k-Anonymity Model for Privacy Preserving Data Publishing. In: KDD 2006, pp. 754–759 (2006)
Xiao, X., Tao, Y.: Anatomy: Simple and Effective Privacy Preservation. In: VLDB, pp. 139–150 (2006)
Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. Int’l Journal on Uncertainty, Fuzziness, and Knowledge-Base Systems 10(5), 571–588 (2002)
Meyerson, A., Williams, R.: On the complexity of optimal k-anonymity. In: PODS, pp. 223–228 (2004)
Michael, H., Gerome, M., David, J., Philipp, W., Siddharth, S.: Anonymizing social networks. Technical report, University of Massachusetts Amherst (2007)
Kun, L., Kamalika, D., Tyrone, G., Hillol, K.: Privacy-Preserving Data Analysis on Graphs and Social Networks. In: Kargupta, H., Han, J., Yu, P., Motwani, R., Kumar, V. (eds.) Next Generation of Data Mining, ch. 21, pp. 419–437. Chapman & Hall/CRC (2008)
Kun, L., Evimaria, T.: Towards identity anonymization on graphs. In: Proceedings of ACM SIGMOD, Vancouver, Canada, pp. 93–106 (2008)
Kapron Bruce, M., Gautam, S., Venkatesh, S.: Social Network Anonymization via Edge Addition. In: ASONAM 2011, pp. 155–162 (2011)
Sean, C., Kapron Bruce, M., Ganesh, R., Gautam, S., Alex, T., Venkatesh, S.: k-Anonymization of Social Networks by Vertex Addition. In: ADBIS (2) 2011, pp. 107–116 (2011)
Bin, Z., Jian, P.: Preserving privacy in social networks against neighborhood attacks. In: Proceedings of the 24th International Conference on Data Engineering (ICDE 2008), pp. 506–515 (2008)
PedarsaniPedram, G.M.: On the Privacy of Anonymized Networks. In: KDD 2011, pp. 1235–1243 (2011)
Wu, W., Xiao, Y., Wang, W., He, Z., Wang, Z.: K-Symmetry Model for Identity Anonymization in Social Networks. In: Proceedings of the 13th International Conference on Extending Database Technology (EDBT 2010), pp. 111–122 (2010)
Wang, G., Liu, Q., Li, F., Yang, S., Wu, J.: Outsourcing Privacy-Preserving Social Networks to Cloud. In: 2013 Proceedings IEEE INFOCOM, pp. 2886–2894 (2013)
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Mohapatra, D., Patra, M.R. (2015). k-degree Closeness Anonymity: A Centrality Measure Based Approach for Network Anonymization. In: Natarajan, R., Barua, G., Patra, M.R. (eds) Distributed Computing and Internet Technology. ICDCIT 2015. Lecture Notes in Computer Science, vol 8956. Springer, Cham. https://doi.org/10.1007/978-3-319-14977-6_29
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DOI: https://doi.org/10.1007/978-3-319-14977-6_29
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