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SCOR: A secure international informatics infrastructure to investigate COVID-19

Author(s)
Raisaro, J LMarino, FrancescoTroncoso-Pastoriza, JuanBeau-Lejdstrom, RaphaelleBellazzi, RiccardoMurphy, RobertBernstam, Elmer VWang, HenryBucalo, MauroChen, YongGottlieb, AssafHarmanci, ArifKim, MiranKim, YejinKlann, JeffreyKlersy, CatherineMalin, Bradley AMéan, MariePrasser, FabianScudeller, LuigiaTorkamani, AliVaucher, JulienPuppala, MamtaWong, Stephen T CFrenkel-Morgenstern, MilanaXu, HuaMusa, Baba MaiyakiHabib, Abdulrazaq GCohen, TrevorWilcox, AdamSalihu, Hamisu MSofia, HeidiJiang, XiaoqianHubaux, JP
Issued Date
2020-11
DOI
10.1093/jamia/ocaa172
URI
https://scholarworks.unist.ac.kr/handle/201301/48345
Fulltext
https://academic.oup.com/jamia/advance-article/doi/10.1093/jamia/ocaa172/5869802
Citation
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, v.27, no.11, pp.1721 - 1726
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.
Publisher
Oxford University Press
ISSN
1067-5027
Keyword (Author)
COVID-19international consortiumsecure data analysishealthcare privacyfederated learning

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