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

J.L. Raisaro, F. Marino, J. Troncoso-Pastoriza, R. Beau-Lejdstrom, R. Bellazzi, R. Murphy, E.V. Bernstam, H. Wang, M. Bucalo, Y. Chen, A. Gottlieb, A. Harmanci, M. Kim, Y. Kim, J. Klann, C. Klersy, B.A. Malin, M. Meán, F. Prasser, L. ScudellerA. Torkamani, J. Vaucher, M. Puppala, S.T.C. Wong, M. Frenkel-Morgenstern, H. Xu, B.M. Musa, A.G. Habib, T. Cohen, A. Wilcox, H.M. Salihu, H. Sofia, X. Jiang, J.P. Hubaux

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)1721-1726
Number of pages6
JournalJournal of the American Medical Informatics Association
Volume27
Issue number11
DOIs
Publication statusPublished - 2020

Keywords

  • COVID-19
  • federated learning
  • healthcare privacy
  • international consortium
  • secure data analysis
  • computer security
  • Coronavirus infection
  • ethics
  • human
  • information dissemination
  • international cooperation
  • machine learning
  • medical research
  • pandemic
  • privacy
  • virus pneumonia
  • Biomedical Research
  • Computer Security
  • Coronavirus Infections
  • Humans
  • Information Dissemination
  • Internationality
  • Machine Learning
  • Pandemics
  • Pneumonia, Viral
  • Privacy

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