An Effective Data Transformation Approach for Privacy Preserving Clustering

Abstract

A new stream of research privacy preserving data mining emerged due to the recent advances in data mining, Internet and security technologies. Data sharing among organizations considered to be useful which offer mutual benefit for business growth. Preserving the privacy of shared data for clustering was considered as the most challenging problem. To overcome the problem, the data owner published the data by random modification of the original data in certain way to disguise the sensitive information while preserving the particular data property. Data transformation techniques played a vital role to preserve privacy in data mining. We put forward an effective approach which defeats the problem of addressing privacy of confidential categorical data in clustering. A set of hybrid data transformations are introduced (HDTTR and HDTSR) and the effectiveness of the approach has been analyzed. A complete analysis of the proposed approach and a formal study of the problem have been done. Our proposed approach illustrates the effectiveness of clustering of sensitive categorical data before and after the transformation.