Quantitatively measuring privacy in interactive query settings within RDBMS framework

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dc.contributor.authorKhan, Muhammad Imran
dc.contributor.authorFoley, Simon N.
dc.contributor.authorO'Sullivan, Barry
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2021-02-22T13:34:52Z
dc.date.available2021-02-22T13:34:52Z
dc.date.issued2020-05-05
dc.date.updated2021-02-22T13:27:41Z
dc.description.abstractLittle attention has been paid to the measurement of risk to privacy in Database Management Systems, despite their prevalence as a modality of data access. This paper proposes PriDe, a quantitative privacy metric that provides a measure (privacy score) of privacy risk when executing queries in relational database management systems. PriDe measures the degree to which attribute values, retrieved by a principal (user) engaging in an interactive query session, represent a reduction of privacy with respect to the attribute values previously retrieved by the principal. It can be deployed in interactive query settings where the user sends SQL queries to the database and gets results at run-time and provides privacy-conscious organizations with a way to monitor the usage of the application data made available to third parties in terms of privacy. The proposed approach, without loss of generality, is applicable to BigSQL-style technologies. Additionally, the paper proposes a privacy equivalence relation that facilitates the computation of the privacy score.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationKhan, M. I., Foley, S. N. and O'Sullivan, B. (2020) 'Quantitatively Measuring Privacy in Interactive Query Settings Within RDBMS Framework', Frontiers in Big Data, 3 (11), (14 pp). doi: 10.3389/fdata.2020.00011en
dc.identifier.doi10.3389/fdata.2020.00011en
dc.identifier.endpage14en
dc.identifier.issn2624-909X
dc.identifier.issued11en
dc.identifier.journaltitleFrontiers In Big Dataen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/11084
dc.identifier.volume3en
dc.language.isoenen
dc.publisherFrontiers Mediaen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.urihttps://www.frontiersin.org/article/10.3389/fdata.2020.00011
dc.rights© 2020 Khan, Foley and O'Sullivan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectElectronic privacyen
dc.subjectData analyticsen
dc.subjectRelational Database Management Systems (RDMS)en
dc.subjectPrivacy scoreen
dc.subjectN-gramen
dc.subjectGDPR - General Data Protection Regulationen
dc.titleQuantitatively measuring privacy in interactive query settings within RDBMS frameworken
dc.typeArticle (peer-reviewed)en
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