Privacy preserving data clustering using a heterogeneous data distortion

Abstract

Modern age computation leads to huge amount of data. The whole data is analysed using data mining. In return, it made its path to disruption of the privacy of data owners. In order to achieve privacy on data we use Privacy Preserving Data Mining (PPDM). But when the privacy is maintained the data utility is decreased and vice versa. So, in order to maintain a balance in both privacy and data utility, Privacy Preserving Data Clustering (PPDC) using a Heterogeneous data distortion is introduced. In this article both original and perturbed data are analysed using K-means and density based clustering techniques and the results are compared to show the balance between privacy and utility of the data. © Springer Nature Singapore Pte Ltd. 2019.