The GA-based algorithms for optimizing hiding sensitive itemsets through transaction deletion
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- 作者：Chun-Wei Lin (1) (2)
Tzung-Pei Hong (3) (4)
Kuo-Tung Yang (3)
Shyue-Liang Wang (5)
1. Innovative Information Industry Research Center (IIIRC) ; Harbin Institute of Technology Shenzhen Graduate School ; Shenzhen ; China
2. Shenzhen Key Laboratory of Internet Information Collaboration ; School of Computer Science and Technology ; Harbin Institute of Technology Shenzhen Graduate School ; Shenzhen ; China
3. Department of Computer Science and Information Engineering ; National University of Kaohsiung ; Kaohsiung ; Taiwan ; Republic of China
4. Department of Computer Science and Engineering ; National Sun Yat-sen University ; Kaohsiung ; Taiwan ; Republic of China
5. Department of Information Management ; National University of Kaohsiung ; Kaohsiung ; Taiwan ; Republic of China - 关键词：Privacy preserving ; Data mining ; Genetic algorithm ; Pre ; large concept ; Evolutionary computation
- 刊名：Applied Intelligence
- 出版年：2015
- 出版时间：March 2015
- 年：2015
- 卷：42
- 期：2
- 页码：210-230
- 全文大小：2,127 KB
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- 刊物主题：Artificial Intelligence and Robotics
Mechanical Engineering
Manufacturing, Machines and Tools - 出版者：Springer Netherlands
- ISSN：1573-7497
文摘
Data mining technology is used to extract useful knowledge from very large datasets, but the process of data collection and data dissemination may result in an inherent threat to privacy. Some sensitive or private information concerning individuals, businesses and organizations has to be suppressed before it is shared or published. Privacy-preserving data mining (PPDM) has become an important issue in recent years. In the past, many heuristic approaches were developed to sanitize databases for the purpose of hiding sensitive information in PPDM, but data sanitization of PPDM is considered to be an NP-hard problem. It is critical to find the balance between privacy protection for hiding sensitive information and maintaining the discovery of knowledge, or even reducing artificial knowledge in the sanitization process. In this paper, a GA-based framework with two optimization algorithms is proposed for data sanitization. A novel evaluation function with three concerned factors is designed to find the appropriate transactions to be deleted in order to hide sensitive itemsets. Experiments are then conducted to evaluate the performance of the proposed GA-based algorithms with regard to different factors such as the execution time, the number of hiding failures, the number of missing itemsets, the number of artificial itemsets, and database dissimilarity.