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dc.contributor.authorKaleli, Cihan
dc.contributor.authorPolat, Hüseyin
dc.date.accessioned2019-10-21T19:44:26Z
dc.date.available2019-10-21T19:44:26Z
dc.date.issued2013
dc.identifier.issn0219-6220
dc.identifier.issn1793-6845
dc.identifier.urihttps://dx.doi.org/10.1142/S0219622013500090
dc.identifier.urihttps://hdl.handle.net/11421/19878
dc.descriptionWOS: 000317850400002en_US
dc.description.abstractProviding recommendations based on distributed data has received an increasing amount of attention because it offers several advantages. Online vendors who face problems caused by a limited amount of available data want to offer predictions based on distributed data collaboratively because they can surmount problems such as cold start, limited coverage, and unsatisfactory accuracy through partnerships. It is relatively easy to produce referrals based on distributed data when privacy is not a concern. However, concerns regarding the protection of private data, financial fears due to revealing valuable assets, and legal regulations imposed by various organizations prevent companies from forming collaborations. In this study, we propose to use random projection to protect online vendors' privacy while still providing accurate predictions from distributed data without sacrificing online performance. We utilize random projection to eliminate the aforementioned issues so vendors can work in partnerships. We suggest privacy-preserving schemes to offer recommendations based on vertically or horizontally partitioned data among multiple companies. The recommended methods are analyzed in terms of confidentiality. We also analyze the superfluous loads caused by privacy concerns. Finally, we perform real data-based trials to evaluate the accuracy of the proposed schemes. The results of our analyses show that our methods preserve privacy, cause insignificant overheads, and offer accurate predictions.en_US
dc.description.sponsorshipTUBITAK [108E221]en_US
dc.description.sponsorshipThis work is supported by grant 108E221 from TUBITAK.en_US
dc.language.isoengen_US
dc.publisherWorld Scientific Publ Co Pte LTDen_US
dc.relation.isversionof10.1142/S0219622013500090en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPrivacyen_US
dc.subjectRandom Projectionen_US
dc.subjectDistributed Dataen_US
dc.subjectRecommendationen_US
dc.subjectPerformanceen_US
dc.titlePrivacy-Preserving Random Projection-Based Recommendations Based on Distributed Dataen_US
dc.typearticleen_US
dc.relation.journalInternational Journal of Information Technology & Decision Makingen_US
dc.contributor.departmentAnadolu Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.identifier.volume12en_US
dc.identifier.issue2en_US
dc.identifier.startpage201en_US
dc.identifier.endpage232en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US]
dc.contributor.institutionauthorKaleli, Cihan


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