Feature Creation based Slicing for Privacy Preserving Data Mining

Abstract

In the digital era vast amount of data are collected and shared for purpose of research and analysis. These data contain sensitive information about the people and organizations which needs to be protected during the process of data mining. This work proposes Feature Creation Based Slicing [FCBS] algorithm for preserving privacy such that sensitive data are not exposed during the process of data mining in Multi Trust Level [MTL] environment. The proposed algorithm applies three layers of privacy preservation using both perturbation and non-perturbation techniques and creates new features from already existing attribute vector. Experiments are performed on real life and benchmarked datasets and the results are compared with the existing slicing and L-diversity algorithms. The results show that privacy preserved datasets generated using the proposed algorithm yields negligible hiding failure while protecting sensitive patterns during association mining and gives comparable utility during classification. Due to feature creation process in the proposed algorithm, linking and known background attacks are prevented. Also, the variance values of the proposed privacy preserved datasets show that they can prevent diversity attacks.