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dc.contributor.authorGüneş, İhsan
dc.contributor.authorBilge, Alper
dc.contributor.authorPolat, Hüseyin
dc.date.accessioned2019-10-21T19:44:24Z
dc.date.available2019-10-21T19:44:24Z
dc.date.issued2013
dc.identifier.issn1976-7277
dc.identifier.urihttps://dx.doi.org/10.3837/tiis.2013.05.019
dc.identifier.urihttps://hdl.handle.net/11421/19872
dc.descriptionWOS: 000320007300019en_US
dc.description.abstractPrivacy-preserving collaborative filtering schemes are becoming increasingly popular because they handle the information overload problem without jeopardizing privacy. However, they may be susceptible to shilling or profile injection attacks, similar to traditional recommender systems without privacy measures. Although researchers have proposed various privacy-preserving recommendation frameworks, it has not been shown that such schemes are resistant to profile injection attacks. In this study, we investigate two memory-based privacy-preserving collaborative filtering algorithms and analyze their robustness against several shilling attack strategies. We first design and apply formerly proposed shilling attack techniques to privately collected databases. We analyze their effectiveness in manipulating predicted recommendations by experimenting on real data-based benchmark data sets. We show that it is still possible to manipulate the predictions significantly on databases consisting of masked preferences even though a few of the attack strategies are not effective in a privacy-preserving environment.en_US
dc.description.sponsorshipTUBITAK [111E218]en_US
dc.description.sponsorshipThis work was partially supported by the Grant 111E218 from TUBITAK.en_US
dc.language.isoengen_US
dc.publisherKsii-Kor Soc Internet Informationen_US
dc.relation.isversionof10.3837/tiis.2013.05.019en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectShillingen_US
dc.subjectPrivacyen_US
dc.subjectRobustnessen_US
dc.subjectRecommendationen_US
dc.subjectProfile Injectionen_US
dc.subjectCollaborative Filteringen_US
dc.titleShilling Attacks Against Memory-Based Privacy-Preserving Recommendation Algorithmsen_US
dc.typearticleen_US
dc.relation.journalKsii Transactions On Internet and Information Systemsen_US
dc.contributor.departmentAnadolu Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.identifier.volume7en_US
dc.identifier.issue5en_US
dc.identifier.startpage1272en_US
dc.identifier.endpage1290en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US]
dc.contributor.institutionauthorBilge, Alper


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