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

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:
2028190
负责人:
Patrick Schaumont
金额:
$4.03万
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
项目状态:
已结题
起止时间:
2020-05-01 至 2021-04-30

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项目成果

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中文摘要
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英文摘要
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.
期刊论文(1)
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科研奖励(0)
会议论文
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
影响因子: --
作者: [Krishnan, A.S., Yang, Y., Schaumont, P]
通讯作者: Schaumont, P
Collaborative: FMitF: Track I: A Principled Approach to Modeling and Analysis of Hardware Fault Attacks on Embedded Software
  • 批准号:
    2219810
  • 项目类别:
    Standard Grant
  • 资助金额:
    $37.45万
  • 财政年份:
    2022
  • 负责人:
    Patrick Schaumont
  • 依托单位:
NSF Student Travel Grant for 2019 Conference on Cryptographic Hardware and Embedded Systems (CHES)
NSF Student Travel Grant for 2018 Conference on Cryptographic Hardware and Embedded Systems
TWC: Small: Secure by Construction: An Automated Approach to Comprehensive Side Channel Resistance
海外基金