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Kim, Miran
Applied Cryptography Lab.
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dc.citation.endPage 1726 -
dc.citation.number 11 -
dc.citation.startPage 1721 -
dc.citation.title JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION -
dc.citation.volume 27 -
dc.contributor.author Raisaro, J L -
dc.contributor.author Marino, Francesco -
dc.contributor.author Troncoso-Pastoriza, Juan -
dc.contributor.author Beau-Lejdstrom, Raphaelle -
dc.contributor.author Bellazzi, Riccardo -
dc.contributor.author Murphy, Robert -
dc.contributor.author Bernstam, Elmer V -
dc.contributor.author Wang, Henry -
dc.contributor.author Bucalo, Mauro -
dc.contributor.author Chen, Yong -
dc.contributor.author Gottlieb, Assaf -
dc.contributor.author Harmanci, Arif -
dc.contributor.author Kim, Miran -
dc.contributor.author Kim, Yejin -
dc.contributor.author Klann, Jeffrey -
dc.contributor.author Klersy, Catherine -
dc.contributor.author Malin, Bradley A -
dc.contributor.author Méan, Marie -
dc.contributor.author Prasser, Fabian -
dc.contributor.author Scudeller, Luigia -
dc.contributor.author Torkamani, Ali -
dc.contributor.author Vaucher, Julien -
dc.contributor.author Puppala, Mamta -
dc.contributor.author Wong, Stephen T C -
dc.contributor.author Frenkel-Morgenstern, Milana -
dc.contributor.author Xu, Hua -
dc.contributor.author Musa, Baba Maiyaki -
dc.contributor.author Habib, Abdulrazaq G -
dc.contributor.author Cohen, Trevor -
dc.contributor.author Wilcox, Adam -
dc.contributor.author Salihu, Hamisu M -
dc.contributor.author Sofia, Heidi -
dc.contributor.author Jiang, Xiaoqian -
dc.contributor.author Hubaux, JP -
dc.date.accessioned 2023-12-21T16:44:50Z -
dc.date.available 2023-12-21T16:44:50Z -
dc.date.created 2020-10-13 -
dc.date.issued 2020-11 -
dc.description.abstract Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secure and privacy-preserving infrastructure to support auditable data sharing and facilitate automated and legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate the latest progress in modern security and federated machine learning algorithms, which are poised to offer solutions. An international group of passionate researchers came together with a joint mission to solve the problem with our finest models and tools. The SCOR Consortium has developed a ready-to-deploy secure infrastructure using world-class privacy and security technologies to reconcile the privacy/utility conflicts. We hope our effort will make a change and accelerate research in future pandemics with broad and diverse samples on an international scale. -
dc.identifier.bibliographicCitation JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, v.27, no.11, pp.1721 - 1726 -
dc.identifier.doi 10.1093/jamia/ocaa172 -
dc.identifier.issn 1067-5027 -
dc.identifier.scopusid 2-s2.0-85089117172 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/48345 -
dc.identifier.url https://academic.oup.com/jamia/advance-article/doi/10.1093/jamia/ocaa172/5869802 -
dc.identifier.wosid 000594986600012 -
dc.language 영어 -
dc.publisher Oxford University Press -
dc.title SCOR: A secure international informatics infrastructure to investigate COVID-19 -
dc.type Article -
dc.description.isOpenAccess TRUE -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass ssci -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor COVID-19 -
dc.subject.keywordAuthor international consortium -
dc.subject.keywordAuthor secure data analysis -
dc.subject.keywordAuthor healthcare privacy -
dc.subject.keywordAuthor federated learning -

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