A Study on Normalization Techniques for Privacy Preserving Data Mining

Abstract Data mining is a prevailing technique which extracts the unfamiliar appealing patterns from large data sets. The extracted facts are utilized in various domains like marketing, weather forecasting, and medical diagnosis. It is very vital that the data gets exposed when the organizations start sharing the data for the mining process and privacy may be breached. Privacy is becoming a more and more significant issue in many data mining applications. Privacy preserving techniques gives a new track to solve this problem. It gives legitimate data mining outcomes without edifying the original data values and thus guarantees privacy as well as accuracy. In this paper we have analyzed the use normalization techniques in achieving privacy. We have compared the results of these techniques and from the experimental outcome it can be concluded that Min-Max normalization have minimum misclassification error. KeywordAccuracy;Clustering;K-Means;Normalization; Privacy.