RAPID: SafePaths: A privacy-first contact tracing solution for early interventions of COVID-19 spread during the first wave and to minimize the second wave of the epidemic

  • Funded by National Science Foundation (NSF)
  • Total publications:0 publications

Grant number: unknown

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2020
    2021
  • Known Financial Commitments (USD)

    $100,000
  • Funder

    National Science Foundation (NSF)
  • Principal Investigator

    Ramesh Raskar
  • Research Location

    United States of America
  • Lead Research Institution

    Massachusetts Institute of Technology
  • Research Priority Alignment

    N/A
  • Research Category

    Epidemiological studies

  • Research Subcategory

    Disease transmission dynamics

  • Special Interest Tags

    Data Management and Data SharingDigital HealthInnovation

  • Study Type

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Unspecified

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

The objective of this project is to develop and deploy a privacy-first digital solution for public health coordination including contact-tracing to curb pandemics like COVID-19 spread. The key is to provide location and context for citizens and public health experts. Current approaches operate on a trade-off between privacy and effectiveness, relying on general public broadcasting that introduces uncertainty in the information extracted or resorting to privacy-violating technologies that risk individual rights against stigmatization and surveillance. This project will break past this dichotomy by developing a technology-based solution for coordinating information on infection and possible transmission through contact-tracing while protecting the privacy rights of viral carriers and unexposed citizens.

Beyond assisting the containment of COVID-19 pandemic by contact tracing, this project will make empirical contributions to the fields of computing, healthcare, crisis response, and more. With privacy preservation being the key aspect of this project, contact tracing is achieved by using encrypted GPS trails and rotating Bluetooth identifiers. In this approach, redacted information of an infected individual is only shared while no information leaves the device of a healthy person. Specifically, this project will advance knowledge regarding: 1.) how cryptographic techniques can be implemented on ubiquitous platforms like smart phones through easy to use apps to efficiently use privatized data without leakage of any sensitive information; 2) how personal-technology solutions to societal crises can effectively influence behavior and consequently affect the outcome of such crises; and 3) how ?split-learning?, a resource efficient distributed AI technique can be implemented with personal information on health, demographic, travel history, spatial context, and real-world engagement to perform private risk-assessment post contact-tracing to reduce false alarm rates. The solution is being built by a consortium of epidemiologists, engineers, data scientists, digital privacy evangelists, professors and researchers from reputable institutions. This is crucial to reduce disruption in socio-economic activity and keep panic under rationally controllable levels in response to future emergencies.

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.