Title: Associative classification rules hiding for privacy preservation

Authors: Juggapong Natwichai, Xingzhi Sun, Xue Li

Addresses: Computer Engineering Department, Faculty of Engineering, Chiang Mai University, 239 Huay Kaew Road, Suthep, Chiang Mai 50200, Thailand. ' IBM Research Laboratory, Building 19 Zhongguancun Software Park, 8 Dongbeiwang WestRoad, Haidian District, Beijing 100193, China. ' School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia

Abstract: Sensitive patterns could be discovered from the given data when the data are shared between business partners. Such patterns should not be disclosed to the other parties. However, the shared data should be credible and trustworthy for their |quality|. In this paper, we address a problem of sensitive classification rule hiding by a data reduction approach. We focus on an important type of classification rules, i.e., associative classification rule. In our context, the impact on data quality generated by data reduction processes is represented by the number of false-dropped rules and ghost rules. To address the problem, we propose a few observations on the reduction approach. Subsequently, we propose a greedy algorithm for the problem based on the observations. Also, we apply two-bitmap indexes to improve the efficiency of the proposed algorithm. Experiment results are presented to show the effectiveness and the efficiency of the proposed algorithm.

Keywords: privacy preservation; associative classification rules; rule hiding; data quality; privacy protection; data reduction.

DOI: 10.1504/IJIIDS.2011.040088

International Journal of Intelligent Information and Database Systems, 2011 Vol.5 No.3, pp.246 - 270

Received: 18 Feb 2010
Accepted: 03 Sep 2010

Published online: 21 Oct 2014 *

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