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Vector Quantization for Privacy Preserving Clustering in Data Mining

Author(s): D.Aruna Kumari | Rajasekhara Rao | M.Suman

Journal: Advanced Computing : an International Journal
ISSN 2229-726X

Volume: 3;
Issue: 6;
Start page: 69;
Date: 2012;
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Keywords: Vector quantization | code book generation | privacy preserving data mining | k-means clustering. 1.

Large Volumes of personal data is regularly collected from different sources and analyzed by differenttypes of applications using data mining algorithms , sharing of these data is useful to the applicationusers.On one hand it is an important asset to business organizations and governments for decision makingat the same time analysing such data opens treats to privacy if not done properly. This paper aims to revealthe information by protecting sensitive data. We are using Vector quantization technique for preservingprivacy. Quantization will be performed on training data samples it will produce transformed data set.This transformed data set does not reveal the sensitive data. And one can apply data mining algorithms ontransformed data and can get accurate results by preserving privacy
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