基于本体的值概化层次数据分析质量评价
Ontology-Based Quality Evaluation of Value Generalization Hierarchies for Data Anonymization
摘要：
在隐私保护数据发布中，使用值泛化层次（VGH）的方法形成了一类重要的匿名化算法。VGH在已发布数据集的实用性中发挥着关键作用，因为它们决定了数据的匿名化如何发生。对于分类属性，必须保持原始数据的语义，以实现更高的效用。尽管如此，语义没有被正式考虑在VGH的规范。此外，没有方法允许用户评估其VGH的质量。在本文中，我们提出了一个测量方案，基于本体论，定量评估质量的VGH，在语义一致性和分类组织，以产生更高质量的匿名化的目的。我们证明，通过案例研究，我们的评估方案可以用来比较多个VGH的质量，可以帮助识别故障VGH。
英文摘要：
In privacy-preserving data publishing, approaches using Value Generalization Hierarchies (VGHs) form an important class of anonymization algorithms. VGHs play a key role in the utility of published datasets as they dictate how the anonymization of the data occurs. For categorical attributes, it is imperative to preserve the semantics of the original data in order to achieve a higher utility. Despite this, semantics have not being formally considered in the specification of VGHs. Moreover, there are no methods that allow the users to assess the quality of their VGH. In this paper, we propose a measurement scheme, based on ontologies, to quantitatively evaluate the quality of VGHs, in terms of semantic consistency and taxonomic organization, with the aim of producing higher-quality anonymizations. We demonstrate, through a case study, how our evaluation scheme can be used to compare the quality of multiple VGHs and can help to identify faulty VGHs.
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