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  • 标题:Emerging Measures in Preserving Privacy for Publishing The Data
  • 本地全文:下载
  • 作者:K.SIVARAMAN
  • 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.
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