Privacy Scoring over OSNs: Shared Data Granularity as a Latent Dimension

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dc.authoridKILIC, Yasir/0000-0001-9666-3746
dc.contributor.authorKılıç, Yasir
dc.contributor.authorInan, Ali
dc.date.accessioned2025-01-06T17:44:04Z
dc.date.available2025-01-06T17:44:04Z
dc.date.issued2023
dc.description.abstractPrivacy scoring aims at measuring the privacy violation risk of a user over an online social network (OSN) based on attribute values shared in the user's OSN profile page and the user's position in the network. Existing studies on privacy scoring rely on possibly biased or emotional survey data. In this study, we work with real-world data collected from the professional LinkedIn OSN and show that probabilistic scoring models derived from the item response theory fit real-world data better than naive approaches. We also introduce the granularity of the data an OSN user shares on her profile as a latent dimension of the OSN privacy scoring problem. Incorporating data granularity into our model, we build the most comprehensive solution to the OSN privacy scoring problem. Extensive experimental evaluation of various scoring models indicates the effectiveness of the proposed solution.
dc.identifier.doi10.1145/3604909
dc.identifier.issn1559-1131
dc.identifier.issn1559-114X
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85178875742
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1145/3604909
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2915
dc.identifier.volume17
dc.identifier.wosWOS:001091665900006
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherAssoc Computing Machinery
dc.relation.ispartofAcm Transactions on The Web
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectPrivacy scoring
dc.subjectonline social network (OSN)
dc.subjectitem response theory (IRT)
dc.subjectdata granularity
dc.subjectLinkedIn
dc.titlePrivacy Scoring over OSNs: Shared Data Granularity as a Latent Dimension
dc.typeArticle

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