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The Scientific World Journal
Volume 2014, Article ID 235837, 12 pages
http://dx.doi.org/10.1155/2014/235837
Research Article

Reducing Side Effects of Hiding Sensitive Itemsets in Privacy Preserving Data Mining

1Innovative Information Industry Research Center (IIIRC), School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China
2Shenzhen Key Laboratory of Internet Information Collaboration, School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China
3Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan
4Department of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung 804, Taiwan

Received 16 January 2014; Accepted 26 February 2014; Published 10 April 2014

Academic Editors: T. Cao and M. Ivanovic

Copyright © 2014 Chun-Wei Lin 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

Data mining is traditionally adopted to retrieve and analyze knowledge from large amounts of data. Private or confidential data may be sanitized or suppressed before it is shared or published in public. Privacy preserving data mining (PPDM) has thus become an important issue in recent years. The most general way of PPDM is to sanitize the database to hide the sensitive information. In this paper, a novel hiding-missing-artificial utility (HMAU) algorithm is proposed to hide sensitive itemsets through transaction deletion. The transaction with the maximal ratio of sensitive to nonsensitive one is thus selected to be entirely deleted. Three side effects of hiding failures, missing itemsets, and artificial itemsets are considered to evaluate whether the transactions are required to be deleted for hiding sensitive itemsets. Three weights are also assigned as the importance to three factors, which can be set according to the requirement of users. Experiments are then conducted to show the performance of the proposed algorithm in execution time, number of deleted transactions, and number of side effects.