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首页> 外文期刊>International Journal of Innovative Computing Information and Control >EVALUATION OF THE DISCLOSURE RISK OF MASKING METHODS DEALING WITH TEXTUAL ATTRIBUTES
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EVALUATION OF THE DISCLOSURE RISK OF MASKING METHODS DEALING WITH TEXTUAL ATTRIBUTES

机译：文本属性处理方法的披露风险评估

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摘要

Record linkage methods evaluate the disclosure risk of revealing confidential information in anonymized datasets that are publicly distributed. Concretely, they measure the capacity of an intruder to link records in the original dataset with those in the masked one. In the past, masking and record linkage methods have been developed focused on numerical or ordinal data. Recently, motivated by the proliferation of textual information, some authors have proposed masking methods to anonymize textual data. Textual attributes should be interpreted according to their semantics, which makes them more difficult to manage and compare than numerical data. In this paper, we propose a new record linkage method specially tailored to accurately evaluate their disclosure risk. Our method, named Semantic Record Linkage, relies on the theory of semantic similarity and uses widely available ontologies to interpret the semantics of data and propose coherent record linkages. Test performed over a real dataset shows that a semantic record linkage method evaluates better the disclosure risk when compared with a non-semantic approach.
机译：记录链接方法评估在公开分发的匿名数据集中泄露机密信息的披露风险。具体来说，它们测量入侵者将原始数据集中的记录与被屏蔽数据中的记录链接的能力。过去，掩蔽和记录链接方法已经针对数字或有序数据进行了开发。近来，由于文本信息的激增，一些作者提出了掩盖方法来匿名化文本数据。文本属性应根据其语义进行解释，这使其比数字数据更难以管理和比较。在本文中，我们提出了一种新的记录链接方法，专门用于准确评估其披露风险。我们的方法称为语义记录链接，它依赖于语义相似性理论，并使用广泛可用的本体来解释数据的语义并提出一致的记录链接。在真实数据集上进行的测试表明，与非语义方法相比，语义记录链接方法可以更好地评估披露风险。

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