On Vector Quantization

Huge Volumes of detailed personal data is continuously collected and analyzed by different types of applications using data mining, analysing such data is beneficial to the application users. It is an important asset to application users like business organizations, governments for taking effective decisions. But analysing such data opens treats to privacy if not done properly. This work aims to reveal the information by protecting sensitive data. Various methods including Randomization, k-anonymity and data hiding have been suggested for the same. In this work, a novel technique is suggested that makes use of LBG design algorithm to preserve the privacy of data along with compression of data. Quantization will be performed on training data it will produce transformed data set. It provides individual privacy while allowing extraction of useful knowledge from data, Hence privacy is preserved. Distortion measures are used to analyze the accuracy of transformed data.