Implementation of Client Controlled Privacy Preserving Data Model for Mining Decision Rules Using Decision Tree and Association Rules

Abstract

The privacy-preserving data mining (PPDM) is one of the techniques which are used for mining data dynamically with preserving privacy of the end data owner. In this paper, a PPDM technique for generating the privacy-preserving decision rules is proposed and implemented. The key motive of presenting this privacy-preserving decision rule mining technique is to demonstrate how securely data is aggregated in the PPDM environment, how securely extract them and consumed them with the help of applications. In addition to comparing the state of art methods for mining privacy preserving decision rules for preparing the future directions of research. Therefore two different data models have used namely decision tree and association rule mining. The conducted experiments demonstrate that decision tree-based techniques are superior to the association rule mining based techniques for mining higher dimensional data with higher accuracy and low resource consumption. Therefore in the near future for extending this data model the two concepts are also introduced in this paper.