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.

中文翻译：

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SCOR：一个安全的国际信息学基础设施，用于调查COVID-19。

摘要

全球流行病需要大量多样的医疗数据来研究各种危险因素，治疗选择和疾病进展模式。尽管许多大型数据联盟计划付出了巨大的努力，但科学界仍然缺乏安全和隐私保护的基础架构来支持可审核的数据共享，并在国际范围内促进自动化和合法合规的联合分析。现有的健康信息学系统并没有融合现代安全性和联合机器学习算法的最新进展，而后者有望提供解决方案。一个国际热心的研究人员小组共同承担了一项任务，即使用我们最好的模型和工具来解决问题。SCOR联盟已使用世界一流的隐私和安全技术开发了易于部署的安全基础结构，以调和隐私/实用程序冲突。我们希望我们的努力能够做出改变，并在国际范围内以广泛而多样的样本加快对未来流行病的研究。