Preservation of Data Privacy Using PCA Based Transformation

Privacy-preserving data mining (PPDM) is one of the recent trends in privacy and security research. Recent advances in data collection, data dissemination and related technologies have inaugurated a new era of research where existing data mining algorithms should be reconsidered from a different point of view, this of privacy preservation. This paper explores all the aspects of privacy issues in datamining, especially related with clustering, and provides a technique for privacy preserving clustering with a hypothetical banking scenario. Here we propose a model for clustering horizontally partitioned or centralized data sets using a simple PCA based transformation approach. The proposed PPC method has been implemented using Matlab and evaluated using synthetic datasets. The proposed privacy preserving transformation preserved the nature of the data even in the transformed form. The classification accuracy while using the transformed data is almost equal to that of the original dataset.