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dc.contributor.authorYigzaw, Kassaye Yitbarek
dc.contributor.authorMichalas, Antonis
dc.contributor.authorBellika, Johan Gustav
dc.date.accessioned2022-09-12T09:18:09Z
dc.date.available2022-09-12T09:18:09Z
dc.date.issued2017-01-03
dc.description.abstractBackground: Techniques have been developed to compute statistics on distributed datasets without revealing private information except the statistical results. However, duplicate records in a distributed dataset may lead to incorrect statistical results. Therefore, to increase the accuracy of the statistical analysis of a distributed dataset, secure deduplication is an important preprocessing step.<p><p> Methods: We designed a secure protocol for the deduplication of horizontally partitioned datasets with deterministic record linkage algorithms. We provided a formal security analysis of the protocol in the presence of semi-honest adversaries. The protocol was implemented and deployed across three microbiology laboratories located in Norway, and we ran experiments on the datasets in which the number of records for each laboratory varied. Experiments were also performed on simulated microbiology datasets and data custodians connected through a local area network. <p>Results: The security analysis demonstrated that the protocol protects the privacy of individuals and data custodians under a semi-honest adversarial model. More precisely, the protocol remains secure with the collusion of up to N − 2 corrupt data custodians. The total runtime for the protocol scales linearly with the addition of data custodians and records. One million simulated records distributed across 20 data custodians were deduplicated within 45 s. The experimental results showed that the protocol is more efficient and scalable than previous protocols for the same problem. <p>Conclusions: The proposed deduplication protocol is efficient and scalable for practical uses while protecting the privacy of patients and data custodians.en_US
dc.identifier.citationYigzaw KY, Michalas A, Bellika JG. Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation. BMC Medical Informatics and Decision Making. 2017;17(1)en_US
dc.identifier.cristinIDFRIDAID 1425857
dc.identifier.doi10.1186/s12911-016-0389-x
dc.identifier.issn1472-6947
dc.identifier.urihttps://hdl.handle.net/10037/26762
dc.language.isoengen_US
dc.publisherBMCen_US
dc.relation.journalBMC Medical Informatics and Decision Making
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2016 The Author(s)en_US
dc.titleSecure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computationen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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