An Approach for data confidentiality and Logical data Security on data release by Multidimensional K-Anonymity Model

Abstract

In our daily routine life there is huge and significant increase in frequently collection of data having different varieties and it growing in huge quantity sometimes we need to share this information and Sharing this huge information is very useful for researchers, Marketing peoples and for business use as this sheared information might be useful to invent new things or to earn money but, Publishing the data may put person privacy on stake and protection of users privacy is a challenging task during micro data release, here we recommend a method which protects users privacy by applying Multidimensional K-Anonymity Method there are to methods single and multidimensional in multidimensional we need to identify level of K-Anonymity and apply Generalization and Suppression methods for users data privacy protection. Also we identify values of k , CAvg,CDM as Quality measures and plot the values of growing value of K to prove that Nearest Neighbor is effective technique for achieving data confidentiality and logical data security on data release.