RAPID: Collaborative: A privacy-preserving contact tracing system for COVID-19 containment and mitigation
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: 2028190
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Key facts
Disease
COVID-19Start & end year
20202021Known Financial Commitments (USD)
$40,345Funder
National Science Foundation (NSF)Principal Investigator
Patrick SchaumontResearch Location
United States of AmericaLead Research Institution
Worcester Polytechnic InstituteResearch Priority Alignment
N/A
Research Category
Epidemiological studies
Research Subcategory
Impact/ effectiveness of control measures
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
Computer and Information Science and Engineering - 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.
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.