Privacy-Preserving Integration of Medical Data.
Medical data are often maintained by different organizations. However, detailed analyses sometimes require these datasets to be integrated without violating patient or commercial privacy. Multiparty Private Set Intersection (MPSI), which is an important privacy-preserving protocol, computes an inter...
| Publicado en: | Journal of Medical Systems Vol. 41; no. 3; pp. 1 - 11 |
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| Autores principales: | , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Mar2017
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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=121441731&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121441731 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Mar2017 vid: 41 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 121441731 121441731 121441731 10.1007/s10916-016-0657-4 121441731 ppf: 1 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Privacy-Preserving Integration of Medical Data. aug: au: Miyaji, Atsuko Nakasho, Kazuhisa Nishida, Shohei affil: Graduate School of Engineering , Osaka University , 2-1 Yamadaoka Suita, Osaka Japan sug: subj: Privacy and Confidentiality Methods Medical Records Data Security Methods Algorithms Human Protocols Computer Simulation Funding Source ab: Medical data are often maintained by different organizations. However, detailed analyses sometimes require these datasets to be integrated without violating patient or commercial privacy. Multiparty Private Set Intersection (MPSI), which is an important privacy-preserving protocol, computes an intersection of multiple private datasets. This approach ensures that only designated parties can identify the intersection. In this paper, we propose a practical MPSI that satisfies the following requirements: The size of the datasets maintained by the different parties is independent of the others, and the computational complexity of the dataset held by each party is independent of the number of parties. Our MPSI is based on the use of an outsourcing provider, who has no knowledge of the data inputs or outputs. This reduces the computational complexity. The performance of the proposed MPSI is evaluated by implementing a prototype on a virtual private network to enable parallel computation in multiple threads. Our protocol is confirmed to be more efficient than comparable existing approaches. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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