A New Profile Based Privacy Measure for Data Publishing
Kumar Vasantha · 2012
The k-anonymity privacy requirement for publishing microdata requires that each equivalence class (i.e., a set of records that are indistinguishable from each other with respect to certain “identifying” attributes) contains at least k records. Recently, several authors have recognized that k-anonymity cannot prevent attribute disclosure. The notion of ‘diversity has been proposed to address this; l-diversity requires that each equivalence class has at least ‘well represented values for each sensitive attribute. In this paper, we follows that l-diversity has a number of limitations. In particular, it is neither necessary nor sufficient to prevent attribute disclosure. Motivated by these limitations, we worked on new notion of privacy called “closeness.”In this paper we are introducing performance based automatic data publishing to multiple users using User Profile Category(UPC) , this method enhances the present flexible privacy model called (n,t)-closeness. We discuss the rationale for using closeness as a privacy measure and illustrate its advantages through examples and experiments.