Efficiently Hiding Sensitive Itemsets with Transaction Deletion Based on Genetic Algorithms
Joint Authors
Hong, Tzung Pei
Lin, Chun-Wei
Zhang, Binbin
Yang, Kuo-Tung
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-09-01
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Lin, Chun-Wei& Zhang, Binbin& Yang, Kuo-Tung& Hong, Tzung Pei. 2014. Efficiently Hiding Sensitive Itemsets with Transaction Deletion Based on Genetic Algorithms. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-13.
https://search.emarefa.net/detail/BIM-1049464
Modern Language Association (MLA)
Lin, Chun-Wei…[et al.]. Efficiently Hiding Sensitive Itemsets with Transaction Deletion Based on Genetic Algorithms. The Scientific World Journal No. 2014 (2014), pp.1-13.
https://search.emarefa.net/detail/BIM-1049464
American Medical Association (AMA)
Lin, Chun-Wei& Zhang, Binbin& Yang, Kuo-Tung& Hong, Tzung Pei. Efficiently Hiding Sensitive Itemsets with Transaction Deletion Based on Genetic Algorithms. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-13.
https://search.emarefa.net/detail/BIM-1049464
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049464