Abstract
A great challenge in privacy preservation is to trade off two important issues: data utility and privacy preservation, in publication of dataset which usually contains sensitive information. Anonymization is a well-represent approach to achieve this, and there exist several anonymity models. Most of those models mainly focuses on protecting privacy exerting identical protection for the whole table with pre-defined parameters. As a result, it could not meet the diverse requirements of protection degrees varied with different sensitive values.Motivated by this, this paper firstly introduces an a-diversity k-anonymity model (ADKAM) to satisfy the diversity deassociation for sensitive values, ant then designs a framework based on an improved microaggregation algorithm, as an alternative to generalization/ suppression to achieve anonymization. By using this framework, we improve the data utility and disclosure risk of privacy disclosure. We conduct several experiments to validate our schemes.
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References
Domingo-Ferrer, J., MartÃnez-Ballesté, A., Mateo-Sanz, J.M., Sebé, F.: Efficient multivariate data-oriented microaggregation. The VLDB Journal 15(4), 355–369 (2006)
Domingo-Ferrer, J., Mateo-Sanz, J.M.: Practical data-oriented microaggregation for statistical disclosure control. IEEE Transactions on Knowledge and Data Engineering 14(1), 189–201 (2002)
Domingo-Ferrer, J., Sebé, F., Solanas, A.: A polynomial-time approximation to optimal multivariate microaggregation. Computers & Mathematics with Applications 55(4), 714–732 (2008)
Domingo-Ferrer, J., Solanas, A., Martinez-Balleste, A.: Privacy in statistical databases: k-anonymity through microaggregation. In: GrC, pp. 774–777 (2006)
Domingo-Ferrer, J., Torra, V.: Ordinal, continuous and heterogeneous k-anonymity through microaggregation. Data Mining and Knowledge Discovery 11(2), 195–212 (2005)
Gedik, B., Liu, L.: Location privacy in mobile systems: A personalized anonymization model. In: Proceedings of 25th IEEE International Conference on Distributed Computing Systems, ICDCS 2005, pp. 620–629. IEEE (2005)
Gedik, B., Liu, L.: Protecting location privacy with personalized k-anonymity: Architecture and algorithms. IEEE Transactions on Mobile Computing 7(1), 1–18 (2008)
Lambert, D.: Measures of disclosure risk and harm. Journal of Official Statistics-Stockholm 9, 313–313 (1993)
Li, N., Li, T., Venkatasubramanian, S.: t-closeness: Privacy beyond k-anonymity and l-diversity. In: ICDE, vol. 7, pp. 106–115 (2007)
Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: l-diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data (TKDD)Â 1(1), 3 (2007)
Meyerson, A., Williams, R.: On the complexity of optimal k-anonymity. In: Proceedings of the Twenty-third ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2004, pp. 223–228. ACM, New York (2004)
Oganian, A., Domingo-Ferrer, J.: On the complexity of optimal microaggregation for statistical disclosure control. Statistical Journal of the United Nations Economic Commission for Europe 18(4), 345–353 (2001)
Panagiotakis, C., Tziritas, G.: Successive group selection for microaggregation. IEEE Transactions on Knowledge and Data Engineering 25(5), 1191–1195 (2013)
Solanas, A., Martinez-Balleste, A., Domingo-Ferrer, J.: V-mdav: a multivariate microaggregation with variable group size. In: 17th COMPSTAT Symposium of the IASC, Rome (2006)
Soria-Comas, J., Domingo-Ferrer, J.: Probabilistic k-anonymity through microaggregation and data swapping. In: 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8 (2012)
Soria-Comas, J., Domingo-Ferrer, J., Sánchez, D., MartÃnez, S.: Enhancing data utility in differential privacy via microaggregation-based k-anonymity. The VLDB Journal, 1–24 (2014)
Sweeney, L.: k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10(05), 557–570 (2002)
Truta, T.M., Vinay, B.: Privacy protection: p-sensitive k-anonymity property. In: ICDE Workshops, p. 94 (2006)
Wong, R.C.-W., Li, J., Fu, A.W.-C., Wang, K.: (α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 754–759. ACM (2006)
Xiao, X., Tao, Y.: Personalized privacy preservation. In: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, pp. 229–240. ACM (2006)
Xu, Y., Wang, K., Zhang, B., Chen, Z.: Privacy-enhancing personalized web search. In: Proceedings of the 16th International Conference on World Wide Web, pp. 591–600. ACM (2007)
Yuan, M., Chen, L., Yu, P.S.: Personalized privacy protection in social networks. Proceedings of the VLDB Endowment 4(2), 141–150 (2010)
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Cheng, L., Cheng, S., Jiang, F. (2015). ADKAM: A-Diversity K-Anonymity Model via Microaggregation. In: Lopez, J., Wu, Y. (eds) Information Security Practice and Experience. ISPEC 2015. Lecture Notes in Computer Science(), vol 9065. Springer, Cham. https://doi.org/10.1007/978-3-319-17533-1_36
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DOI: https://doi.org/10.1007/978-3-319-17533-1_36
Publisher Name: Springer, Cham
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