基于最坏情况分布的背景知识分析
Anonymization with Worst-Case Distribution-Based Background Knowledge
摘要：
背景知识是隐私保护数据发布中的一个重要因素。基于分布的背景知识是背景知识中研究得最多的一种。然而，据我们所知，有没有现有的工作考虑在最坏的情况下，基于分布的背景知识，我们的意思是，对手有准确的知识，根据一些元组属性的敏感值的分布。考虑这种最坏的情况是必要的，因为我们不能忽视任何违约的可能性。在本文中，我们提出了一个算法，匿名数据集，以保护个人隐私，考虑到这个背景知识。我们证明了我们提出的算法生成的匿名数据集保护个人隐私。我们的实证研究表明，我们的方法保留了较高的效用，同时公布的数据。
英文摘要：
Background knowledge is an important factor in privacy preserving data publishing. Distribution-based background knowledge is one of the well studied background knowledge. However, to the best of our knowledge, there is no existing work considering the distribution-based background knowledge in the worst case scenario, by which we mean that the adversary has accurate knowledge about the distribution of sensitive values according to some tuple attributes. Considering this worst case scenario is essential because we cannot overlook any breaching possibility. In this paper, we propose an algorithm to anonymize dataset in order to protect individual privacy by considering this background knowledge. We prove that the anonymized datasets generated by our proposed algorithm protects individual privacy. Our empirical studies show that our method preserves high utility for the published data at the same time.
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