Compression in Privacy preserving Data Mining

Large Volumes of detailed personal data is regularly collected and analyzed by applications using data mining, sharing of these data is beneficial 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 based on LBG Design algorithm for preserving privacy with the help of Codebook. Quantization will be performed on training data samples it will produce transformed data set. This transformed data set does not reveal the original data. Hence privacy is preserved