Ordinal, continuous and heterogeneous k-anonymity through microaggregation
Ordinal, continuous and heterogeneous k-anonymity through microaggregation
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DOI：
10.1007/s10618-005-0007-5
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发表时间：
2005-09-01
影响因子：
4.8
通讯作者：
Torra, V
中科院分区：
文献类型：
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作者：
Domingo-Ferrer, J;Torra, V
k-匿名是解决个人数据（微数据）保护中数据效用与被调查者隐私之间紧张关系的一个有用概念。然而，文献中提出的实现k-匿名的泛化和抑制方法并不同样适用于所有类型的属性：(i)泛化/抑制是名义分类属性的少数几种可能性之一；（ii）有序直言属性只是一种不总是保持有序的可能性；（iii）并且它完全不适合连续属性，因为它会使它们失去其数值意义。由于导致披露（因此需要k-匿名化）的属性可能是名义的、有序的，也可能是连续的，因此设计k-匿名化过程以尽可能地保留每种属性类型的语义是很重要的。我们在本文中建议使用分类微聚集作为名义和序数k匿名化的泛化/抑制的替代方案；我们还提出了连续微聚合作为连续k匿名化的方法。
k-Anonymity is a useful concept to solve the tension between data utility and respondent privacy in individual data (microdata) protection. However, the generalization and suppression approach proposed in the literature to achieve k-anonymity is not equally suited for all types of attributes: (i) generalization/suppression is one of the few possibilities for nominal categorical attributes; (ii) it is just one possibility for ordinal categorical attributes which does not always preserve ordinality; (iii) and it is completely unsuitable for continuous attributes, as it causes them to lose their numerical meaning. Since attributes leading to disclosure (and thus needing k-anonymization) may be nominal, ordinal and also continuous, it is important to devise k-anonymization procedures which preserve the semantics of each attribute type as much as possible. We propose in this paper to use categorical microaggregation as an alternative to generalization/suppression for nominal and ordinal k-anonymization; we also propose continuous microaggregation as the method for continuous k-anonymization.