Privacy Preserving of Associative Classification and Heuristic Approach

Abstract

In the era of data explosion, privacy preserving has become a necessary task 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. Also, for classification mining, each classification approach may use different approach to deliver knowledge. Therefore, data quality metric for the classification task should be tailored to a specific type of classification. In this paper, we focus on maintaining the data quality in the scenarios which the transformed data will be used to build associative classification models. We propose a data quality metric for such the associative classification. Also, we propose a heuristic approach to preserve the privacy and maintain the data quality. Subsequently, we validate our proposed approaches with experiments.

Preservation of the Privacy for Multiple Custodian Systems With Rule Sharing

“…Shaneck et al (2006) addressed the issue of secure multi Science Publications JCS party computation which formed the kernel of many data mining applications. Harnsamut et al (2008) focused on maintaining the data quality in the scenarios in which the transformed data was used to build associative classification models. Lin and Chen (2008) decided which instances of training dataset were support vectors, i.e., the necessarily informative instances to form the classifier.…”