Inverse document frequency-based sensitivity scoring for privacy analysis

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dc.contributor.authorCoban, Onder
dc.contributor.authorInan, Ali
dc.contributor.authorOzel, Selma Ayse
dc.date.accessioned2025-01-06T17:44:04Z
dc.date.available2025-01-06T17:44:04Z
dc.date.issued2022
dc.description.abstractPrivacy risk analysis of online social network (OSN) users aims at generating a risk score for each OSN user such that higher scores potentially imply a greater risk of privacy violation. Privacy risk analysis is typically carried out over a response matrix (R) where any matrix element r(ij) indicates the portion of the OSN that the user i shares his/her attribute j. Most of the existing work relies on the mathematical framework of item response theory to derive sensitivity and visibility components from R. In this study, we propose interpreting R to be a term-document matrix and consequently suggest using the inverse document frequency (IDF) method as the sensitivity component. Experiments performed on both synthetic and real-world datasets show that the proposed IDF-based method can be used as a sensitivity component.
dc.identifier.doi10.1007/s11760-021-02013-1
dc.identifier.endpage743
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85113905417
dc.identifier.scopusqualityQ2
dc.identifier.startpage735
dc.identifier.urihttps://doi.org/10.1007/s11760-021-02013-1
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2913
dc.identifier.volume16
dc.identifier.wosWOS:000691156000001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofSignal Image and Video Processing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectPrivacy risk analysis
dc.subjectOnline social networks
dc.subjectInverse-document frequency
dc.titleInverse document frequency-based sensitivity scoring for privacy analysis
dc.typeArticle

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