PRIVACY PRESERVING DM USING K-MEANSLBG FOR VECTOR QUNTIZATION (KVQ) AND NOISE ADDITION

Abstract

Perturbation method is a very important technique in privacy preserving data mining. In this technique, the
tradeoff is in between loss of information and preservation of privacy. The question is, how much are the users willing to
compromise their privacy? This is a choice that changes from individual to individual. The data comes from a heterogeneous
environments including financial, library,shopping, medicaland telephonerecords.As it is possible due to the rapid growth in
database,computing, andnetworking technologiesso such data can be integrated and analyzed digitally. In order to share
data while preserving privacy data owner must come up with a solution which achieves the dual goal of privacy preservation
as well as accurate clustering result. Trying to give solution for this we implemented vector quantization approach piecewise
on the datasets which segmentize each row of datasets and quantization approach is performed on each segment using K
means clustering algorithm.