期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2014
卷号：2
期号：5
出版社：S&S Publications
摘要：The information in the way of publishing requires some of the privacy measures. The k-anonymityprivacy requirement for publishing micro data requires that each equivalence class (i.e., a set of records that areindistinguishable 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 l -diversity has been proposed to address this; l -diversity requires that each equivalence class has at least ` wellrepresentedvalues for each sensitive attribute. In this article, we show that l -diversity has a number of limitations.In particular, it is neither necessary nor sufficient to prevent attribute disclosure. Motivated by these limitations, wepropose a new notion of privacy called “closeness”. Here it present the base model t- closeness, which requires thatthe distribution of a sensitive attribute in any equivalence class is close to the distribution of the attribute in theoverall table (i.e., the distance between the two distributions should be no more than a threshold t). Then propose amore flexible privacy model called (n, t)-closeness that offers higher utility and it describes desiderata for designinga distance measure between two probability distributions and present two distance measures. This paper discussesthe rationale for using closeness as a privacy measure and illustrates its advantages through examples andexperiments.
关键词：privacy preservation; data anonymization; data publishing; data security.
