Efficiently Hiding Sensitive Itemsets with Transaction Deletion Based on Genetic Algorithms

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ID: 36734
2014
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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.
Reference Key
lin2014efficientlythe Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lin, Chun-Wei;Zhang, Binbin;Yang, Kuo-Tung;Hong, Tzung-Pei;
Journal the scientific world journal
Year 2014
DOI
DOI not found
URL
http://dx.doi.org/10.1155/2014/398269
Keywords Keywords not found

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