Privacy preservation for associative classification: An approximation algorithm

• 主要作者: Natwichai J.
• 格式: Article
• 語言: English
• 出版: 2014
• 在線閱讀: http://www.scopus.com/inward/record.url?eid=2-s2.0-84860416250&partnerID=40&md5=9d6e5341e36aa7a72488fda33525a70a; http://cmuir.cmu.ac.th/handle/6653943832/1586
• 機構: Chiang Mai University

Privacy is one of the most important issues when dealing with the individual data. Typically, given a data set and a data-processing target, the privacy can be guaranteed based on the pre-specified standard by applying privacy data-transformation algorithms. Also, the utility of the data set must be considered while the transformation takes place. However, the data-transformation problem such that a privacy standard must be satisfied and the impact on the data utility must be minimised is an NP-hard problem. In this paper, we propose an approximation algorithm for the data transformation problem. The focused data processing addressed in this paper is classification using association rule, or associative classification. The proposed algorithm can transform the given data sets with O(κ log κ)-approximation factor with regard to the data utility comparing with the optimal solutions. The experiment results show that the algorithm is both effective and efficient comparing with the optimal algorithm and the other two heuristic algorithms. © 2011 Inderscience Enterprises Ltd.