Learning and synchronized privacy preserving frequent pattern mining

被引：0
|
作者：
Guo Y.-H. [1 ,2 ]
Tong Y.-H. [2 ]
Tang S.-W. [2 ]
Wu L.-D. [3 ]
机构：
[1] Department of Information Technology, University of International Relations
[2] Key Laboratory of Machine Perception (Ministry of Education), Peking University
[3] Department of Computing Science, University of Alberta
来源：
Ruan Jian Xue Bao/Journal of Software | 2011年 / 22卷 / 08期
关键词：
Frequent pattern mining; Learning-based; Privacy preserving; Randomization; Supervised;
D O I：
10.3724/SP.J.1001.2011.04000
中图分类号：
学科分类号：
摘要：
To improve the accuracy of mining results, this paper proposes a method of privacy preserving frequent pattern mining, based on sample learning and synchronized randomization of itemset (LS-PPFM). The method utilizes the data of individuals who do not care privacy. First, the data that does not need protecting are learned, and some strongly associated items are obtained. Then, when the data is randomized, the associated items are bound together and randomized synchronously to try to keep their potential associations. Experimental results show that compared with independent randomization, LS-PPFM can achieve significant improvements on accuracy, while losing a little privacy. © 2011 ISCAS.
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收藏
页码：1749 / 1760
页数：11
相关论文
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