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Ontology-Based Quality Evaluation of Value Generalization Hierarchies for Data Anonymization
Date Issued
2014-09
Date Available
2015-04-08T11:23:49Z
Abstract
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.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Web versions
Language
English
Status of Item
Peer reviewed
Conference Details
Privacy in Statistical Databases, Eivissa, Balearic Islands, 17-19 September, 2014
This item is made available under a Creative Commons License
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Name
PSD_AyalaRivera.pdf
Size
365.32 KB
Format
Adobe PDF
Checksum (MD5)
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