Data Science Group and Precision Medicine Unit, Lausanne University Hospital, Lausanne, Switzerland.
Laboratory for Data Security, EPFL, Lausanne, Switzerland.
J Am Med Inform Assoc. 2020 Nov 1;27(11):1721-1726. doi: 10.1093/jamia/ocaa172.
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.
全球性大流行病需要大量多样化的医疗保健数据来研究各种风险因素、治疗方案和疾病进展模式。尽管许多大型数据联盟计划做出了巨大努力，但科学界仍然缺乏安全且保护隐私的基础架构来支持可审计的数据共享，并促进在国际范围内进行自动化和合法合规的联合分析。现有的健康信息学系统没有纳入现代安全和联合机器学习算法的最新进展，这些进展有望提供解决方案。一组充满激情的国际研究人员齐聚一堂，共同利用我们最好的模型和工具来解决这个问题。SCOR 联盟使用世界级的隐私和安全技术开发了一种现成的可部署的安全基础架构，以协调隐私/效用冲突。我们希望我们的努力将带来改变，并在未来的大流行中加速国际范围内广泛多样样本的研究。
