Enhancedt-closeness for balancing utility and privacy. (1st January 2013) - Babu, Korra Sathya
Driven by mutual benefits or guidelines, the collected data from various sources like hospitals, government agencies and private corporations are published in the internet. Publishing data without anonymising violates individual privacy, even if they are onymous or pseudonymous. A model that works on preserving privacy is t -closeness. This model overcomes attribute disclosure but is vulnerable to identity disclosure. The distance metric used in t -closeness does not satisfy all distance metrics like probability scaling. This article discusses two issues. First, on improving the utility of the data by incorporating Bhattacharya distance metric and secondly, to overcome the identity disclosure issue of t -closeness with a proposed algorithm. In the first issue the result show that utility of the dataset is improved without degrading the privacy. The result for second issue shows that the discernibility metric cost for proposed method is better than ( n, t )-closeness.