title: Efficiently hiding sensitive itemsets with transaction deletion based on genetic algorithms.
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.
authors.affiliation: Innovative Information Industry Research Center (IIIRC), School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China ; Shenzhen Key Laboratory of Internet Information Collaboration, School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China.
authors.forename: Chun-Wei
authors.identifier: 0000-0001-8768-9709
authors.initials: CW
authors.lastname: Lin
authors.affiliation: Medical School, Shenzhen University, Shenzhen 518060, China.
authors.forename: Binbin
authors.initials: B
authors.lastname: Zhang
authors.affiliation: Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan.
authors.forename: Kuo-Tung
authors.initials: KT
authors.lastname: Yang
authors.affiliation: Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan ; Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan.
authors.forename: Tzung-Pei
authors.initials: TP
authors.lastname: Hong
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods* / Q000706:statistics & numerical data; D003219:Confidentiality; D057225:Data Mining / Q000379:methods* / Q000706:statistics & numerical data; D033181:Information Dissemination / Q000379:methods*; D015203:Reproducibility of Results