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dc.contributor.authorYigzaw, Kassaye Yitbarek
dc.contributor.authorMichalas, Antonis
dc.contributor.authorBellika, Johan Gustav
dc.date.accessioned2017-03-01T11:21:21Z
dc.date.available2017-03-01T11:21:21Z
dc.date.issued2016-08-12
dc.description.abstractCollecting data via a questionnaire and analyzing them while preserving respondents' privacy may increase the number of respondents and the truthfulness of their responses. It may also reduce the systematic differences between respondents and non-respondents. In this paper, we propose a privacy- preserving method for collecting and analyzing survey responses using secure multi-party computation. The method is secure under the semi-honest adversarial model. The proposed method computes a wide variety of statistics. Total and stratified statistical counts are computed using the secure protocols developed in this paper. Then, additional statistics, such as a contingency table, a chi-square test, an odds ratio, and logistic regression, are computed within the R statistical environment using the statistical counts as building blocks. The method was evaluated on a questionnaire data set of 3158 respondents sampled for a medical study and simulated questionnaire data sets of up to 50 000 respondents. The computation time for the statistical analyses linearly scales as the number of respondents increases. The results show that the method is efficient and scalable for practical use. It can also be used for other applications in which categorical data are collected.en_US
dc.descriptionSource: <a href=http://dx.doi.org/10.1109/ACCESS.2016.2599851>doi: 10.1109/ACCESS.2016.2599851</a>en_US
dc.identifier.citationYigzaw KY, Michalas A, Bellika JG. Secure and scalable statistical computation of questionnaire data in R. IEEE Access. 2016;4:4635-4645en_US
dc.identifier.cristinIDFRIDAID 1397197
dc.identifier.doi10.1109/ACCESS.2016.2599851
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/10037/10398
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.journalIEEE Access
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/IKTPLUSS/ 248150/Norway/ Assessing the feasibility of the Learning Healthcare System toolboxen_US
dc.relation.urihttp://ieeexplore.ieee.org/document/7542506/
dc.rights.accessRightsopenAccessen_US
dc.subjectbloom filteren_US
dc.subjectprivacyen_US
dc.subjectquestionnaireen_US
dc.subjectstatistical analysisen_US
dc.subjectsecure multi-party computationen_US
dc.subjectsecret sharingen_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Sikkerhet og sårbarhet: 424en_US
dc.subjectVDP::Mathematics and natural science: 400::Information and communication science: 420::Security and vulnerability: 424en_US
dc.subjectVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550en_US
dc.subjectVDP::Technology: 500::Information and communication technology: 550en_US
dc.titleSecure and scalable statistical computation of questionnaire data in Ren_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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