Ontology-Based Quality Evaluation of Value Generalization Hierarchies for Data Anonymization

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Title: Ontology-Based Quality Evaluation of Value Generalization Hierarchies for Data Anonymization
Authors: Ayala-Rivera, Vanessa
McDonagh, Patrick
Cerqueus, Thomas
Murphy, Liam, B.E.
Permanent link: http://hdl.handle.net/10197/6458
Date: Sep-2014
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.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Keywords: Data anonymization;Value generalization hierarchies;Privacy-preserving data publishing
Language: en
Status of Item: Peer reviewed
Conference Details: Privacy in Statistical Databases, Eivissa, Balearic Islands, 17-19 September, 2014
Appears in Collections:Computer Science Research Collection

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