T-rotation: Multiple Publications of Privacy Preserving Data Sequence

  • Youdong Tao
  • Yunhai Tong
  • Shaohua Tan
  • Shiwei Tang
  • Dongqing Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5139)


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.


Category Attribute Multiple Publication Continuous Period Hierarchy Tree Anonymity Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Youdong Tao
    • 1
  • Yunhai Tong
    • 1
  • Shaohua Tan
    • 1
  • Shiwei Tang
    • 1
  • Dongqing Yang
    • 1
  1. 1.Key Laboratory of Machine Perception (Peking University), Ministry of EducationBeijingChina

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