Privacy Preserving Data Mining Based on Vector Quantization
Huge Volumes of detailed personal data is continuously collected and analyzed by different types of applications using data mining, analyzing 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 analyzing such data opens treats to privacy if not done properly. This paper 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 paper, 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.