T-rotation: Multiple Publications of Privacy Preserving Data Sequence

Authors:
Youdong Tao;Yunhai Tong;Shaohua Tan;Shiwei Tang;Dongqing Yang
Affiliations:
Key Laboratory of Machine Perception (Peking University), Ministry of Education, Beijing, China 100871;Key Laboratory of Machine Perception (Peking University), Ministry of Education, Beijing, China 100871;Key Laboratory of Machine Perception (Peking University), Ministry of Education, Beijing, China 100871;Key Laboratory of Machine Perception (Peking University), Ministry of Education, Beijing, China 100871;Key Laboratory of Machine Perception (Peking University), Ministry of Education, Beijing, China 100871
Venue:
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Year:
2008

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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.