Micro aggregation is a technique for disclosure limitation aimed at protecting the privacy of data subjects in micro data releases, like releasing medical data, census data etc… It has been subjected to generalization and suppression to generate k-anonymous data sets, where the identity of each subject is hidden within a group of k subjects. Unlike generalization, micro aggregation perturbs the data and this additional masking freedom allows improving data utility in several. Existing algorithms like k-anonymity and t-closeness is based on generalization and suppression k-anonymity does not deal with   attribute disclosure and hence   the work focuses on closeness. This paper proposes and shows how to use micro aggregation to generate n,t close data sets. The advantages of micro aggregation with n,t-closeness are analyzed, and the  ultimate aim of the project is to  make comparative analysis  and evaluate micro aggregation algorithms for t-closeness and n,t closeness .  There are many real-life situations in which personal data is stored: (i) Electronic commerce results in the automated collection of large amounts of consumer data. These data, which are gathered by many companies, are shared with subsidiaries and partners. (ii) Health care is a very sensitive sector with strict regulations. In the U.S., the Privacy Rule of the Health Insurance Portability and Accountability Act (HIPAA) requires the strict regulation of protected health information for use in medical research. In most western countries, the situation is similar.