Privacy preservation in transaction databases based on anatomy technique
Yingjie Wu, Shangbin Liao, Xiaowen Ruan, Xiaodong Wang · 2010
This paper considers the problem of privacy preserving transaction data publishing. Transaction data are usually useful for data mining. While it is high-dimensional data, traditional anonymization techniques such as generalization and suppression are not suitable. In this paper, we present a novel technique based on anatomy technique and propose a simple linear-time anonymous algorithm that meets the l-diversity requirement. The simulation experiments on real datasets and the results of association rules mining on the anonymous transaction data showed that our algorithm can safely and efficiently preserve the privacy in transaction data publication, while ensuring high utility of the released data.