Vector Quantization for Privacy Preserving Clustering in Data Mining
Devarakonda Aruna Kumari · Advanced Computing An International Journal · 2012
Large Volumes of personal data is regularly collected from different sources and analyzed by different types of applications using data mining algorithms , sharing of these data is useful to the application users.On one hand it is an important asset to business organizations and governments for decision making at the same time analysing such data opens treats to privacy if not done properly.This paper aims to reveal the information by protecting sensitive data.We are using Vector quantization technique for preserving privacy.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 on transformed data and can get accurate results by preserving privacy KEYWORDSVector quantization, code book generation, privacy preserving data mining ,k-means clustering.