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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© 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
Publisher Name: Springer, Berlin, Heidelberg
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