An approximation algorithm for privacy preservation of associative classification

• Main Author: Natwichai J.
• Format: Conference or Workshop Item
• Language: English
• Published: 2014
• Online Access: http://www.scopus.com/inward/record.url?eid=2-s2.0-77954920947&partnerID=40&md5=8d5b6b36fafd86a0d968aaa2c8227811; http://cmuir.cmu.ac.th/handle/6653943832/1499
• Institution: Chiang Mai University

Privacy is one of the most important issues when the data are to be processed. Typically, given a dataset and a data processing goal, the privacy can be guaranteed by the pre-specified standard by applying privacy data-transformation algorithms. Furthermore, the utility of the dataset must be considered while the transformation takes place. Such data transformation problem such that a privacy standard must be met and the utility must be optimized 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 datasets with O(k log k)-approximation utility comparing with the optimal solutions. The experiment results show that the algorithm can work effectively comparing with the optimal algorithm and the other heuristic algorithm. Also, the proposed algorithm is very efficient.