Improving the Utility of Anonymized Datasets through Dynamic Evaluation of Generalization Hierarchies

DC FieldValueLanguage
dc.contributor.authorAyala-Rivera, Vanessa-
dc.contributor.authorCerqueus, Thomas-
dc.contributor.authorMurphy, Liam, B.E.-
dc.contributor.authorThorpe, Christina-
dc.date.accessioned2017-09-18T12:48:57Z-
dc.date.available2017-09-18T12:48:57Z-
dc.date.issued2016-07-30-
dc.identifier.urihttp://hdl.handle.net/10197/8767-
dc.descriptionIEEE 17th International Conference on Information Reuse and Integration (IRI), Pittsburgh, PA, USA, July, 2016en
dc.description.abstractThe dissemination of textual personal information has become a key driver for innovation and value creation. However, due to the possible content of sensitive information, this data must be anonymized, which can reduce its usefulness for secondary uses. One of the most used techniques to anonymize data is generalization. However, its effectiveness can be hampered by the Value Generalization Hierarchies (VGHs) used to dictate the anonymization of data, as poorly-specified VGHs can reduce the usefulness of the resulting data. To tackle this problem, we propose a metric for evaluating the quality of textual VGHs used in anonymization. Our evaluation approach considers the semantic properties of VGHs and exploits information from the input datasets to predict with higher accuracy (compared to existing approaches) the potential effectiveness of VGHs for anonymizing data. As a consequence, the utility of the resulting datasets is improved without sacrificing the privacy goal. We also introduce a novel rating scale to classify the quality of the VGHs into categories to facilitate the interpretation of our quality metric for practitioners.en
dc.description.sponsorshipScience Foundation Irelanden
dc.language.isoenen
dc.publisherIEEEen
dc.rights© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksen
dc.subjectAnonymizationen
dc.subjectPrivacyen
dc.subjectData publishingen
dc.subjectData qualityen
dc.subjectGeneralization hierarchiesen
dc.subjectData semanticsen
dc.titleImproving the Utility of Anonymized Datasets through Dynamic Evaluation of Generalization Hierarchiesen
dc.typeConference Publicationen
dc.internal.authorcontactothervanessa.ayalarivera@ucd.ie-
dc.statusPeer revieweden
dc.identifier.doi10.1109/IRI.2016.13-
dc.neeo.contributorAyala-Rivera|Vanessa|aut|-
dc.neeo.contributorCerqueus|Thomas|aut|-
dc.neeo.contributorMurphy|Liam, B.E.|aut|-
dc.neeo.contributorThorpe|Christina|aut|-
dc.internal.rmsid788687900-
dc.date.updated2017-08-11T14:23:20Z-
dc.rights.licensehttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/en
item.grantfulltextopen-
item.fulltextWith Fulltext-
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