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The Scientific World Journal
Volume 2014 (2014), Article ID 686151, 10 pages
http://dx.doi.org/10.1155/2014/686151
Research Article

Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response

Web Science Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Received 8 December 2013; Accepted 24 February 2014; Published 30 March 2014

Academic Editors: B. Johansson and L. Xiao

Copyright © 2014 Chongjing Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining. Some works have been proposed to handle this kind of problem. In this paper, we introduce a personalized privacy problem, in which different attributes may need different privacy levels protection. To solve this problem, we give a personalized privacy-preserving method by using the randomized response technique. By providing different privacy levels for different attributes, this method can get a higher accuracy on frequent itemset mining than the traditional method providing the same privacy level. Finally, our experimental results show that our method can have better results on the frequent itemset mining while preserving personalized privacy.