Expanding COVID-19 symptom screening to retail, restaurants, and schools by preserving privacy using relaxed digital signatures

  1. Brandon Jew
  2. Alexis Korb
  3. Paul Lou
  4. Jeffrey N. Chiang
  5. Ulzee An
  6. Amit Sahai
  7. Eran Halperin
  8. Eleazar Eskin

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Abstract

Symptom screening is a widely deployed strategy to mitigate the COVID-19 pandemic and many public health authorities are mandating its use by employers for all employees in the workplace. While symptom screening has the benefit of reducing the number of infected individuals in the workplace, it raises some inherently difficult privacy issues as a traditional approach requires the employer to collect symptom data from each employee which is essentially medical information. In this paper, we describe a system to implement Cryptographic Anonymous Symptom Screening (CASS) which allows for individuals to perform COVID symptom screening anonymously while avoiding the privacy issues of traditional approaches. In the system, individuals report their symptoms without any identifying information and are issued a completion certificate. This certificate contains a cryptographic code which certifies that the certificate was obtained from the screener after reporting no symptoms. The codes can be verified using a cryptographic algorithm which is publicly available. A standard cryptography approach to implement such a system would be to use digital signatures. Unfortunately, standard digital signatures have some limitations for this application in that the signatures are often hundreds of characters long and if the signature contains the name of the individual, then there is also a risk of compromising privacy. In our approach, we develop and utilize a relaxed digital signature scheme to provide 16 character long codes and handle names using equivalence classes which helps preserve privacy. Both of these extensions technically compromise the security but in a way that is negligible for this application. Our system can either serve the function of standard symptom screening system approaches for employees, but can also extend symptom screening to non-employees such as visitors or customers. In this case, the system can be utilized in retail, restaurants and schools to ensure that everyone in the physical space, including employees, customers, visitors and students have performed symptom screening.

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  1. Version published to 10.1101/2020.08.06.20169839v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.08.06.20169839: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.08.06.20169839v1 on medRxiv