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.

与COVID-19战斗的关键工具是一个接触式追踪系统，可以识别过去与确认案件密切接触的个人。这样的机制可以使这些人感到震惊，以便他们自愿自愿进行自我保证。此外，当这些人开始出现症状时，接触式追踪系统可以优先考虑测试，这将更有效地利用有限的测试能力并为受感染的人提供早期治疗。但是，由于美国和许多西方国家的严格隐私保护法律，部署这种系统是一个挑战。为了解决这个紧迫的问题，该项目建立了一个保存隐私的接触跟踪系统，名为Covid Tecter。 COVID探测器依靠智能手机跟踪患者和健康人员的过去位置，并利用加密计算，以确保在接触式跟踪计算过程中不会暴露私人数据。该项目满足了以隐私友好方式支持在现代，复杂和高度联系的世界中追踪用户联系的广泛需求。目前，在平衡公共卫生和个人用户隐私的背景下，此讨论非常相关。虽然有一些现有的Covid-Covid-tractrac跟踪应用程序，但用户/患者身份的匿名性是保护用户隐私的最佳选择。 COVID探测器是第一个提供对用户/患者身份和用户/患者轨迹数据的强大保护。 Covid-detector使用同构加密技术将Ciphertext域中的健康使用者与健康用户的轨迹相匹配，以便健康的用户可以确定其感染风险水平。 匹配过程既不向任何一方揭示健康的用户和患者的位置数据。在轨迹跟踪中使用同态加密技术是新颖的，并且以前尚未应用。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。

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