A novel heuristic algorithm for privacy preserving of associative classification

Abstract

Since individual data are being collected everywhere in the era of data explosion, privacy preserving has become a necessity for any data mining task. Therefore, data transformation to ensure privacy preservation is needed. Meanwhile, the transformed data must have quality to be used in the intended data mining task, i.e. the impact on the data quality with regard to the data mining task must be minimized. However, the data transformation problem to preserve the data privacy while minimizing the impact has been proven as an NP-hard. In this paper, we address the problem of maintaining the data quality in the scenarios which the transformed data will be used to build associative classification models. We propose a novel heuristic algorithm to preserve the privacy and maintain the data quality. Our heuristic is guided by the classification correction rate (CCR) of the given datasets. Our proposed algorithm is validated by experiments. From the experiments, the results show that the proposed algorithm is not only efficient, but also highly effective. © 2008 Springer Berlin Heidelberg.