Profile matching across online social networks

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buir.contributor.authorAyday, Erman
dc.citation.epage70en_US
dc.citation.spage54en_US
dc.citation.volumeNumber12282 LNCSen_US
dc.contributor.authorHalimi, A.en_US
dc.contributor.authorAyday, Ermanen_US
dc.contributor.editorMeng, W.
dc.contributor.editorGollmann, D.
dc.contributor.editorJensen, C. D.
dc.contributor.editorZhou, J.
dc.coverage.spatialCopenhagen, Denmarken_US
dc.date.accessioned2021-03-05T12:03:25Z
dc.date.available2021-03-05T12:03:25Z
dc.date.issued2020
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionDate of Conference: 24-26 August 2020en_US
dc.descriptionConference Name: 22nd International Conference on Information and Communications Security, ICICS 2020en_US
dc.description.abstractIn this work, we study the privacy risk due to profile matching across online social networks (OSNs), in which anonymous profiles of OSN users are matched to their real identities using auxiliary information about them. We consider different attributes that are publicly shared by users. Such attributes include both strong identifiers such as user name and weak identifiers such as interest or sentiment variation between different posts of a user in different platforms. We study the effect of using different combinations of these attributes to profile matching in order to show the privacy threat in an extensive way. The proposed framework mainly relies on machine learning techniques and optimization algorithms. We evaluate the proposed framework on three datasets (Twitter - Foursquare, Google+ - Twitter, and Flickr) and show how profiles of the users in different OSNs can be matched with high probability by using the publicly shared attributes and/or the underlying graphical structure of the OSNs. We also show that the proposed framework notably provides higher precision values compared to state-of-the-art that relies on machine learning techniques. We believe that this work will be a valuable step to build a tool for the OSN users to understand their privacy risks due to their public sharings.en_US
dc.description.provenanceSubmitted by Zeynep Aykut (zeynepay@bilkent.edu.tr) on 2021-03-05T12:03:25Z No. of bitstreams: 1 Profile_matching_across_online_social_networks.pdf: 482141 bytes, checksum: b426832d9f3b98ffe927c70ae9b20156 (MD5)en
dc.description.provenanceMade available in DSpace on 2021-03-05T12:03:25Z (GMT). No. of bitstreams: 1 Profile_matching_across_online_social_networks.pdf: 482141 bytes, checksum: b426832d9f3b98ffe927c70ae9b20156 (MD5) Previous issue date: 2020en
dc.identifier.doi10.1007/978-3-030-61078-4_4en_US
dc.identifier.isbn9783030610777
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11693/75838
dc.language.isoEnglishen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.isversionofhttps://dx.doi.org/10.1007/978-3-030-61078-4_4en_US
dc.source.titleLecture Notes in Computer Scienceen_US
dc.subjectSocial networksen_US
dc.subjectProfile matchingen_US
dc.subjectDeanonymizationen_US
dc.titleProfile matching across online social networksen_US
dc.typeConference Paperen_US

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