Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation.
Background: 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 sta...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 17; pp. 1 - 20 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
1/3/2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=120579986&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120579986 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 1/3/2017 vid: 17 pid: 24147 pub: BioMed Central artinfo: ui: 120579986 120579986 NLM28049465 120579986 10.1186/s12911-016-0389-x NLM28049465 120579986 ppf: 1 ppct: 19 formats: tig: atl: Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation. aug: au: Yigzaw, Kassaye Yitbarek Michalas, Antonis Bellika, Johan Gustav affil: Department of Computer Science, UiT The Arctic University of Norway, 9037 Tromsø, Norway sug: subj: Statistics Standards Privacy and Confidentiality Standards Medical Record Linkage Standards ab: Background: 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.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.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.Conclusions: The proposed deduplication protocol is efficient and scalable for practical uses while protecting the privacy of patients and data custodians. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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