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

File(s)
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Author(s)
Ayala-Rivera, Vanessa 
McDonagh, Patrick 
Cerqueus, Thomas 
Murphy, Liam, B.E. 
Uri
http://hdl.handle.net/10197/6458
Date Issued
September 2014
Date Available
08T11:23:49Z April 2015
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
Keywords
  • Data anonymization

  • Value generalization ...

  • Privacy-preserving da...

Web versions
http://unescoprivacychair.urv.cat/psd2014/
Language
English
Status of Item
Peer reviewed
Description
Privacy in Statistical Databases, Eivissa, Balearic Islands, 17-19 September, 2014
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Owning collection
Computer Science Research Collection
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Mar 31, 2023
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