PRIVACY PRESERVATION FOR HEALTHCARE SYSTEM USING T-CLOSENESS THROUGH MICROAGGREGATION
Sonu V. Khapekar, Lomesh Kautik Ahire · International journal of advance research and innovative ideas in education · 2017
The preservation of privacy of published microdata is essential to prevent the sensitive information of individuals from being disclosed. Several privacy models are used for protecting the privacy of microdata. Microaggregation is a technique for disclosure limitation aimed at protecting the privacy of data subjects in microdata releases. It has been used as an alternative 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, microaggregation perturbs the data and this additional masking freedom allows improving data utility in several ways, such as increasing data granularity, reducing the impact of outliers, and avoiding discretization of numerical data. k-Anonymity, on the other side, does not protect against attribute disclosure, which occurs if the variability of the confidential values in a group of k subjects is too small . In this paper, the preservation of privacy of microdata released in healthcare system is focused through microaggregation by using t-closeness which is a more flexible privacy model assuring strictest privacy. Existing algorithms to generate t-close data sets are based on generalization and suppression. This paper proposes, how to use microaggregation in healthcare system to generate k-anonymous t-close data sets. The advantages of microaggregation are analyzed, and the microaggregation algorithm for k-anonymous t-closeness is presented. The microaggregation by using t-closeness proves an effective tool for protecting the privacy of the sensitive attributes in the healthcare system.