RAPID: Collaborative: A privacy-preserving contact tracing system for COVID-19 containment and mitigation

RAPID：协作：用于遏制和缓解 COVID-19 的隐私保护接触者追踪系统

• 批准号: 2028174
• 负责人: Yaling Yang
• 金额: $ 6.07万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028174&HistoricalAwards=false
• 关键词: RAPID; Collaborative; privacy; preserving; contact

A crucial tool in the fight with COVID-19 is a contact tracing system that can identify individuals who had close contacts with confirmed cases in the past. Such a mechanism can alarm these individuals so that they can voluntarily self-quarantine. In addition, when these individuals start to have symptoms, a contact tracing system can prioritize them for testing, which will make more efficient use of the limited test capacity and provide early treatment for infected individuals. However, due to strict privacy-protection laws in US and many western countries, it is a challenge to deploy such a system. To solve this urgent problem, this project builds a privacy-preserving contact tracing system, named COVID Detector. COVID Detector relies on smartphones to track both patient and healthy person's past locations and leverages cryptographic computations to ensure that no private data is exposed during the contact tracing computation. This project addresses the broader need to support, in a privacy-friendly manner, user contact tracing in a modern, complex and highly-connected world. This discussion is presently highly relevant in the context of balancing public health and individual user privacy.While there are a few existing contact tracing apps for COVID-19, anonymity of user/patient identity is the best that they can provide to protect user privacy. COVID Detector is the first that provides strong protection of both user/patient identity and user/patient trajectory data. COVID-Detector uses homomorphic encryption techniques to match the trajectory of confirmed COVID-19 patients with that of healthy users in the ciphertext domain, so that healthy users can determine their infection risk level. The matching process reveals neither healthy users' nor patients' location data to any party. The use of homomorphic encryption technologies in trajectory tracking is novel and has not been applied before.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

抗击COVID-19的一个关键工具是接触者追踪系统，该系统可以识别过去与确诊病例有密切接触的个人。这种机制可以警告这些人，使他们能够自愿自我隔离。此外，当这些人开始出现症状时，接触者追踪系统可以优先对他们进行检测，这将更有效地利用有限的检测能力，并为感染者提供早期治疗。然而，由于美国和许多西方国家严格的隐私保护法律，部署这样的系统是一个挑战。为了解决这个迫切的问题，本项目建立了一个隐私保护的接触者追踪系统，名为COVID Detector。COVID Detector依靠智能手机来跟踪患者和健康人的过去位置，并利用加密计算来确保在接触追踪计算期间没有私人数据被暴露。该项目满足了在现代、复杂和高度互联的世界中以隐私友好的方式支持用户联系人追踪的更广泛需求。目前，在平衡公共卫生和个人用户隐私的背景下，这一讨论非常相关。虽然现有的COVID-19接触者追踪应用程序很少，但用户/患者身份的匿名性是它们可以提供的最好的保护用户隐私的方法。COVID Detector是第一个为用户/患者身份和用户/患者轨迹数据提供强有力保护的产品。COVID-Detector使用同态加密技术，在密文域中将确诊COVID-19患者的轨迹与健康用户的轨迹进行匹配，以便健康用户确定其感染风险级别。 匹配过程既不向任何一方透露健康用户的位置数据，也不向任何一方透露患者的位置数据。在轨迹跟踪中使用同态加密技术是一种新颖的技术，以前从未应用过。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Risk and Architecture Factors in Digital Exposure Notification

数字暴露通知中的风险和架构因素

• DOI: 10.1007/978-3-030-60939-9_21
• 发表时间: 2020
• 期刊: and Simulation (SAMOS 2020
• 作者: Krishnan, A.S.;Yang, Y.;Schaumont, P
• 通讯作者: Schaumont, P