K-anonymous Association Rule Hiding
In the paper we point out that the released dataset of an as sociation rule hiding method may have severe privacy prob lem since they all achieve to minimize the side effects on the original dataset. We show that an attacker can discover the hidden sensitive association rules with high confidence when there is not enough blindage. We give a detailed analysis of the attack and propose a novel association rule hiding metric, K-anonymous. Based on the K-anonymous metric, we present a framework to hide a group of sensitive association rules while it is guaranteed that the hidden rules are mixed with at least other K-1 rules in the specific re gion. Several heuristic algorithms are proposed to achieve the hiding process. Experiment results are reported to show the effectiveness and efficiency of the proposed approaches.
Association Rule Hiding k-anonymity
Zutao Zhu Wenliang Du
Department of Electrical Engineering and Computer Science Syracuse University, Syracuse, NY, USA 13244
国际会议
北京
英文
305-309
2010-04-13（万方平台首次上网日期，不代表论文的发表时间）