SCOR: A secure international informatics infrastructure to investigate COVID-19
Jean Louis Raisaro, Francesco Marino, Juan Ramón Troncoso-Pastoriza, Raphaëlle Beau‐Lejdstrom, Riccardo Bellazzi, Robert Murphy, Elmer V. Bernstam, Henry E. Wang, Mauro Bucalo, Yong Chen, Assaf Gottlieb, Arif Harmanci, Miran Kim, Yejin Kim, Jeffrey G. Klann, Catherine Klersy, Bradley Malin, Marie Méan, Fabian Praßer, Luigia Scudeller, Ali Torkamani, Julien Vaucher, Mamta Puppala, Stephen T.C. Wong, Milana Frenkel‐Morgenstern, Hua Xu, Baba Maiyaki Musa, Abdulrazaq G. Habib, Trevor Cohen, Adam Wilcox, Hamisu M. Salihu, Heidi J. Sofia, Xiaoqian Jiang, Jean‐Pierre Hubaux
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.