Achieving Privacy in Data Mining Using Normalization

To extract the previously unknown patterns from a large data set is the ultimate goal of any data mining algorithm. Some private or confidential information may be revealed as part of data mining process. In this paper we use min-max normalization approach for preserving privacy during the mining process. We sanitize the original data using min-max normalization approach before publishing. For experimental purpose we have used k-means algorithm and from our results it is evident that our approach preserves both privacy and accuracy.