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dc.contributor.authorKaleli, Cihan
dc.contributor.authorPolat, Hüseyin
dc.date.accessioned2019-10-21T19:44:30Z
dc.date.available2019-10-21T19:44:30Z
dc.date.issued2012
dc.identifier.issn0160-5682
dc.identifier.issn1476-9360
dc.identifier.urihttps://dx.doi.org/10.1057/jors.2011.76
dc.identifier.urihttps://hdl.handle.net/11421/19893
dc.descriptionWOS: 000303920200011en_US
dc.description.abstractData collected for providing recommendations can be partitioned among different parties. Offering distributed data-based predictions is popular due to mutual advantages. It is almost impossible to present trustworthy referrals with decent accuracy from split data only. Meaningful outcomes can be drawn from adequate data. Those companies with distributed data might want to collaborate to produce accurate and dependable recommendations to their customers. However, they hesitate to work together or refuse to collaborate because of privacy, financial concerns, and legal issues. If privacy-preserving measures are provided, such data holders might decide to collaborate for better predictions. In this study, we investigate how to provide predictions based on vertically distributed data (VDD) among multiple parties without deeply jeopardizing their confidentiality. Users are first grouped into various clusters off-line using self-organizing map clustering while protecting the online vendors' privacy. With privacy concerns, recommendations are produced based on partitioned data using a nearest neighbour prediction algorithm. We analyse our privacy-preserving scheme in terms of confidentiality and supplementary costs. Our analysis shows that our method offers recommendations without greatly exposing data holders' privacy and causes negligible superfluous costs because of privacy concerns. To evaluate the scheme in terms of accuracy, we perform real-data-based experiments. Our experiment results demonstrate that the scheme is still able to provide truthful predictions. Journal of the Operational Research Society (2012) 63, 826-838. doi:10.1057/jors.2011.76 Published online 21 September 2011en_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.publisherPalgrave Macmillan LTDen_US
dc.relation.isversionof10.1057/jors.2011.76en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPrivacyen_US
dc.subjectVertically Distributed Dataen_US
dc.subjectPredictionen_US
dc.subjectSomen_US
dc.titleSOM-based recommendations with privacy on multi-party vertically distributed dataen_US
dc.typearticleen_US
dc.relation.journalJournal of the Operational Research Societyen_US
dc.contributor.departmentAnadolu Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.identifier.volume63en_US
dc.identifier.issue6en_US
dc.identifier.startpage826en_US
dc.identifier.endpage838en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US]
dc.contributor.institutionauthorKaleli, Cihan


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