Abstract
Data anonymization techniques based on enhanced privacy principles have been the focus of intense research in the last few years. All existing methods achieving privacy principles assume implicitly that the data objects to be anonymized are given once and fixed, which makes it unsuitable for time evolving data. However, in many applications, the real world data sources are dynamic. In such dynamic environments, the current techniques may suffer from poor data quality and/or vulnerability to inference. In this paper, we investigate the problem of updating large time-evolving microdata based on the sophisticated l-diversity model, in which it requires that every group of indistinguishable records contains at least l distinct sensitive attribute values; thereby the risk of attribute disclosure is kept under 1/l. We analyze how to maintain the l-diversity against time evolving updating. The experimental results show that the updating technique is very efficient in terms of effectiveness and data quality.
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References
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)
Aggarwal, G., Feder, T., Kenthapadi, K., Motwani, R., Panigrahy, R., Thomas, D., Zhu, A.: Approximation algorithms for k-anonymity. Journal of Privacy Technology, paper number 20051120001
Bayardo, R., Agrawal, R.: Data privacy through optimal k-anonymity. In: Proceedings of the 21st International Conference on Data Engineering, ICDE (2005)
Byun, J., Kamra, A., Bertino, E., Li, N.: Efficient k-Anonymization using Clustering Techniques. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 188–200. Springer, Heidelberg (2007)
Fung, B., Wang, K., Yu, P.: Top-down specialization for information and privacy preservation. In: Proc. of the 21st International Conference on Data Engineering (ICDE 2005), Tokyo, Japan (2005)
LeFevre, K., DeWitt, D., Ramakrishnan, R.: Incognito: Efficient Full-Domain k-Anonymity. In: ACM SIGMOD International Conference on Management of Data (June 2005)
Li, N., Li, T., Venkatasubramanian, S.: t-Closeness: Privacy Beyond k-Anonymity and l-Diversity. In: ICDE 2007, pp. 106–115 (2007)
Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: l-Diversity: Privacy beyond k-anonymity. In: ICDE 2006 (2006)
Meyerson, A., Williams, R.: On the complexity of optimal k-anonymity. In: Proc. of the 23rd ACM-SIGMOD-SIGACT-SIGART Symposium on the Principles of Database Systems, Paris, France, pp. 223–228 (2004)
Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases, University of Califonia, Irvine (1998), www.ics.uci.edu/-mlearn/MLRepository.html
Samarati, P.: Protecting respondents’ identities in microdata release. IEEE Transactions on Knowledge and Data Engineering 13(6), 1010–1027 (2001)
Sun, X., Li, M., Wang, H., Plank, A.: An efficient hash-based algorithm for minimal k-anonymity problem. In: 31st Australasian Computer Science Conference (ACSC 2008), Wollongong, NSW, Australia, pp. 101–107. CRPIT 74 (2008)
Sun, X., Wang, H., Li, J.: On the complexity of restricted k-anonymity problem. In: Zhang, Y., Yu, G., Bertino, E., Xu, G. (eds.) APWeb 2008. LNCS, vol. 4976, pp. 287–296. Springer, Heidelberg (2008)
Sun, X., Wang, H., Li, J., Traian, T.M., Li, P.: (p + , α)-sensitive k-anonymity: a new enhanced privacy protection model. In: 8th IEEE International Conference on Computer and Information Technology (IEEE-CIT 2008), July 8-11, pp. 59–64 (2008)
Sweeney, L.: k-anonymity: A Model for Protecting Privacy. International Journal on Uncertainty Fuzziness Knowledge-based Systems 10(5), 557–570 (2002)
Traian, T.M., Bindu, V.: Privacy Protection: l-diversity Property. In: International Workshop of Privacy Data Management (PDM 2006); In Conjunction with 22th International Conference of Data Engineering (ICDE), Atlanta (2006)
Truta, T.M.: Alina Campan, k-Anonymization Incremental Maintenance and Optimization Techniques. In: ACM Symposium on Applied Computing (SAC 2007), special track on Data Mining, Seoul, Korea (2007)
Winkler, W.E.: Advanced Methods for Record Linkage. In: Proceedings of the Section on Survey Research Methods, American Statistical Society, pp. 467–472 (1994)
Wong, R., Li, J., Fu, A., Wang, K.: (α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing. In: KDD 2006, pp. 754–759 (2006)
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Sun, X., Wang, H., Li, J. (2008). L-Diversity Based Dynamic Update for Large Time-Evolving Microdata. In: Wobcke, W., Zhang, M. (eds) AI 2008: Advances in Artificial Intelligence. AI 2008. Lecture Notes in Computer Science(), vol 5360. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89378-3_47
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DOI: https://doi.org/10.1007/978-3-540-89378-3_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89377-6
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