Privacy Preserving Data Mining

Abstract

Now a day's detailed personal data from large data bases is regularly collected and analyzed by many applications with data mining, some times 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 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. Bit Error rate and Distortion measures are used to analyze the accuracy of compressed data. © Springer International Publishing Switzerland 2014.