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The Scientific World Journal
Volume 2014, Article ID 398269, 13 pages
http://dx.doi.org/10.1155/2014/398269
Research Article

Efficiently Hiding Sensitive Itemsets with Transaction Deletion Based on Genetic Algorithms

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
3Medical School, Shenzhen University, Shenzhen 518060, China
4Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan
5Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan

Received 28 May 2014; Revised 7 August 2014; Accepted 8 August 2014; Published 1 September 2014

Academic Editor: Shifei Ding

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 used to mine meaningful and useful information or knowledge from a very large database. Some secure or private information can be discovered by data mining techniques, thus resulting in an inherent risk of threats to privacy. Privacy-preserving data mining (PPDM) has thus arisen in recent years to sanitize the original database for hiding sensitive information, which can be concerned as an NP-hard problem in sanitization process. In this paper, a compact prelarge GA-based (cpGA2DT) algorithm to delete transactions for hiding sensitive itemsets is thus proposed. It solves the limitations of the evolutionary process by adopting both the compact GA-based (cGA) mechanism and the prelarge concept. A flexible fitness function with three adjustable weights is thus designed to find the appropriate transactions to be deleted in order to hide sensitive itemsets with minimal side effects of hiding failure, missing cost, and artificial cost. Experiments are conducted to show the performance of the proposed cpGA2DT algorithm compared to the simple GA-based (sGA2DT) algorithm and the greedy approach in terms of execution time and three side effects.