Classification Techniques for Geometric Data Perturbation in Multiplicative Data Perturbation

Abstract

It is very important to be able to find out useful information from huge amount of data. In this paper we address the privacy problem against unauthorized secondary use of information. To do so, we introduce a family of geometric data transformation methods (GDTMs) which ensure that the mining process will not violate privacy up to a certain degree of security. We focus primarily on privacy preserving data classification methods. Our proposed methods distort only sensitive numerical attributes to meet privacy requirements, while preserving general features for classification analysis. Our experiments demonstrate that our methods are effective and provide acceptable values in practice for balancing privacy and accuracy. This paper focuses on Geometric Data Perturbation to analyse large data sets