Distance Based Transformation for Privacy Preserving Data Mining Using Hybrid Transformation

Abstract

Data mining techniques are used to retrieve the knowledge from large databases that helps the organizations to establish the business effectively in the competitive world. Sometimes, it violates privacy issues of individual customers. This paper addresses the problem of privacy issues related to the individual customers and also propose a transformation technique based on a Walsh-Hadamard transformation (WHT) and Rotation. The WHT generates an orthogonal matrix, it transfers entire data into new domain but maintain the distance between the data records these records can be reconstructed by applying statistical based techniques i.e. inverse matrix, so this problem is resolved by applying Rotation transformation. In this work, we increase the complexity to unauthorized persons for accessing original data of other organizations by applying Rotation transformation. The experimental results show that, the proposed transformation gives same classification accuracy like original data set. In this paper we compare the results with existing techniques such as Data perturbation like Simple Additive Noise (SAN) and Multiplicative Noise (MN), Discrete Cosine Transformation (DCT), Wavelet and First and Second order sum and Inner product Preservation (FISIP) transformation techniques. Based on privacy measures the paper concludes that proposed transformation technique is better to maintain the privacy of individual customers.

A Combined Clustering and Geometric Data Perturbation Approach for Enriching Privacy Preservation of Healthcare Data in Hybrid Clouds

“…At first consider the cluster esteem k=3 and the underlying cluster mean is m1 = 23, m2 = 33, m3 = 48, so the high-dimensional medicinal services information are changed over into three gatherings in light of their comparability work, K1= {23, 25, 28, 30}, K2 = {33, 32, 35, 36} and K3 = {48, 42, 46, 50}. On the following stage, we need to process the centroid of each cluster gathering, K1= (23,25,28,30)/4= 26.5, K2 = (33, 32, 35, 36) /4= 34 and K3 = (48, 42, 46, 50)/4=46.5. Yet, we can't be certain that the cluster partitioning is correct or off-base.…”