Closeness A New Privacy Measure For Data Publishing

Technology Used: Java / J2EE

Knowledge and Data Engineering, 2010

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; `-diversity requires that each equivalence class has at least ` well-represented values for each sensitive attribute. In this article, we show that `-diversity has a number of limitations. In particular, it is neither necessary nor sufficient to prevent attribute disclosure. Motivated by these limitations, we propose a new notion of privacy called “closeness”. We first present the base model t-closeness, which requires that the distribution of a sensitive attribute in any equivalence class is close to the distribution of the attribute in the overall table (i.e., the distance between the two distributions should be no more than a threshold t). We then propose a more flexible privacy model called (n, t)-closeness that offers higher utility. We describe our desiderata for designing a distance measure between two probability distributions and present two distance measures. We discuss the rationale for using closeness as a privacy measure and illustrate its advantages through examples and experiments.

Thanks for installing the Bottom of every post plugin by Corey Salzano. Contact me if you need custom WordPress plugins or website design.

One thought on “Closeness A New Privacy Measure For Data Publishing

  • June 18, 2013 at 2:34 am

    please explain domain of closeness a new privacy measure for data publishing


Leave a Reply

Your email address will not be published. Required fields are marked *