Microaggregation is a technique for revelation limitation aimed at fortifying the seclusion of data subjects into micro data releases.like releasing medical data,census data etc. It has been subjected to generalization and repression to generate k-nameless data sets.Where the identity of each subject is invisible with in a group of k subjects.Unlike generalization ,microaggregation confusion the data and this additional conceal freedom allows upgrading data usefulness in several. Existing algorithms like k-nameless and t-reticence based on generalization and repression.k-nameless does not deal with reticence. This paper proposes and shows how to use microaggregation to generate n,t close data sets.The advantages of microaggregation with n,t reticence are examined,and the eventual aim of the project is to make relative examine and assess microaggregation algorithms for t-reticence and n,t reticence.
S.Nathiya, U.Gowrisankar (2016).
"ENHANCED K-ANONYMITY WITH PRIVACY PRESERVATION USING MICROAGGREGATION".
International Journal of Contemporary Research in Computer Science and Technology,
2(3), pp. 630-632.
