Abstract
In privacy preserving data publishing, most current methods are limited only to the static data which are released once and fixed. However, in real dynamic environments, the current methods may become vulnerable to inference. In this paper, we propose the t-rotation method to process this continuously growing dataset in an effective manner. T-rotation mixes t continuous periods to form the dataset and then anonymizes. It avoids the inference by the temporal background knowledge and considerably improves the anonymity quality.
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References
Aggarwal, G., Feder, T., Kenthapadi, K., Motwani, R., Panigrahy, R., Thomas, D., Zhu, A.: Anonymizing Tables. In: The 10th International Conference on Database Theory, pp. 246–258 (2005)
Byun, J.W., Sohn, Y., Bertino, E., Li, N.: Secure Anonymization for Incremental Datasets. In: Secure Data Management, 3rd VLDB Workshop, pp. 48–63 (2006)
LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Incognito: Efficient Full-domain K-anonymity. In: ACM International Conference on Management of Data, pp. 49–60 (2005)
Machanavajjhala, A., Gehrke, J., Kifer, D.: l-diversity: Privacy beyond K-anonymity. In: The 22nd International Conference on Data Engineering, pp. 24–35 (2006)
Pei, J., Xu, J., Wang, Z., Wang, W., Wang, K.: Maintaining K-anonymity against Incremental Updates. In: 19th International Conference on Scientific and Statistical Database Management, pp. 5–14 (2007)
Samarati, P.: Protecting Respondents’ Identities in Microdata Release. IEEE Transactions on Knowledge and Data Engineering 13, 1010–1027 (2001)
Sweeney, L.: Achieving K-anonymity Privacy Protection Using Generalization and Suppression. International Journal on Uncertainty, Fuzziness and Knowledge Based Systems 10, 571–588 (2002)
UCI Machine Learning Repository, http://www.ics.uci.edu/~mlearn/MLRepository.html
Wang, K., Fung, B.C.M.: Anonymizing Sequential Releases. In: 12th ACM SIGKDD, pp. 414–423 (2006)
Wong, R.C., Li, J., Fu, A.W., Wang, K. (α, k)-anonymity: an Enhanced K-anonymity Model for Privacy-preserving Data Publishing. In: 12th ACM SIGKDD, pp. 754–759 (2006)
Xiao, X., Tao, Y.: Anatomy: Simple and Effective Privacy Preservation. In: The 32nd international conference on Very large data bases, pp. 139–150 (2006)
Xiao, X., Tao, Y.: M-Invariance: Towards Privacy Preserving Re-publication of Dynamic Datasets. In: ACM International Conference on Management of Data, pp. 689–700 (2007)
Xu, J., Wang, W., Pei, J., Wang, X., Shi, B., Fu, A.W.: Utility-based Anonymization Using Local Recoding. In: 12th ACM SIGKDD, pp. 785–790 (2006)
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© 2008 Springer-Verlag Berlin Heidelberg
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Tao, Y., Tong, Y., Tan, S., Tang, S., Yang, D. (2008). T-rotation: Multiple Publications of Privacy Preserving Data Sequence. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2008. Lecture Notes in Computer Science(), vol 5139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88192-6_49
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DOI: https://doi.org/10.1007/978-3-540-88192-6_49
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