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新兴的措施保护隐私发布数据

文摘

信息的发布需要一些隐私的措施。k-anonymity隐私要求发布微数据要求每个等价类(即。一组记录,相互区别对某些“识别”属性)包含至少k记录。最近,一些作者已经认识到k-anonymity无法阻止属性披露。l -多样性的概念提出了解决这一问题;l多样性要求每个等价类至少wellrepresented每个敏感属性的值。在本文中,我们表明,l多样性有很多局限性。特别是,它是既无必要,也不足以防止属性披露。出于这些限制,我们提出一个新的隐私的概念叫做“亲密”。这里的基本模型t -亲密,这要求一个敏感属性的分布在任何等价类接近属性的分布在整个表(即。, the distance between the two distributions should be no more than a threshold t). Then propose a more flexible privacy model called (n, t)-closeness that offers higher utility and it describes desiderata for designing a distance measure between two probability distributions and present two distance measures. This paper discusses the rationale for using closeness as a privacy measure and illustrates its advantages through examples and experiments.

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