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Privacy Preserving Clustering by Hybrid Data Transformation Approach

By M. Naga Lakshmi, Dr. K. S and Hya Rani

Abstract

Abstract-- Numerous organizations collect and share large amounts of data due to the proliferation of information technologies and internet. The information extracted from these databases through data mining process may reveal private information of individuals. Privacy preserving data mining is a new research area, which allows sharing of privacy-sensitive data for analysis purpose. In this paper a hybrid data transformation method is proposed for privacy preserving clustering in centralized database environment by taking the advantage of two existing techniques Principle Component Analysis (PCA) and Non negative Matrix Factorization (NMF). The experimental results proved that the proposed hybrid method protects private data of individuals and also providing valid clustering results

Topics: clustering, Principle component analysis, Non negative Matrix Factorization, Hybrid Method
Year: 2014
OAI identifier: oai:CiteSeerX.psu:10.1.1.413.6959
Provided by: CiteSeerX
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