COVID-19 Contact Tracing and Privacy: A Longitudinal Study of Public Opinion LUCY SIMKO, Paul G. Allen School of Computer Science & Engineering, University of Washington JACK LUCAS CHANG, Information School, University of Washington MAGGIE JIANG, Paul G. Allen School of Computer Science & Engineering, University of Washington RYAN CALO, School of Law, University of Washington FRANZISKA ROESNER, Paul G. Allen School of Computer Science & Engineering, University of Washington TADAYOSHI KOHNO, Paul G. Allen School of Computer Science & Engineering, University of Washington There is growing use of technology-enabled contact tracing, the process of identifying potentially infected COVID-19 patients by notifying all recent contacts of an infected person. Governments, technology companies, and research groups alike have been working towards releasing smartphone apps, using IoT devices, and distributing wearable technology to automatically track łclose contactsž and identify prior contacts in the event an individual tests positive. However, there has been signiicant public discussion about the tensions between efective technology-based contact tracing and the privacy of individuals. To inform this discussion, we present the results of seven months of online surveys focused on contact tracing and privacy, each with 100 participants. Our irst surveys were on April 1 and 3, 2020, before the irst peak of the virus in the US, and we continued to conduct the surveys weekly for 10 weeks (through June), and then fortnightly through November, adding topical questions to relect current discussions about contact tracing and COVID-19. Our results present the diversity of public opinion and can inform policy makers, technologists, researchers, and public health experts on whether and how to leverage technology to reduce the spread of COVID-19, while considering potential privacy concerns. Additional Key Words and Phrases: privacy, security, usable security, contact tracing, longitudinal, COVID-19 1 INTRODUCTION Technology companies, university research groups, and governments have been diligently working to deploy COVID-19 contact tracing apps, for which adoption has been slow [9, 18]. Prior work has determined that contact tracing apps are most efective when used by the majority of a population [22, 26, 64]; however, some have raised security and privacy concerns (e.g., [15, 86]) as well as broader concerns about eicacy (e.g., [85]). Our research seeks to provide to the scientiic, technology, and policy communities an informed understanding of the public’s values, concerns, and opinions about the use of automated contact tracing technologies. We argue neither for nor against automated contact tracing in this work, but instead we ofer a summary of public opinion on potential contact tracing scenarios since many regions have already implemented automated contact tracing programs or are moving towards them. We ask the following research questions: Authors’ addresses: Lucy Simko, Paul G. Allen School of Computer Science & Engineering, University of Washington; Jack Lucas Chang, Information School, University of Washington; Maggie Jiang, Paul G. Allen School of Computer Science & Engineering, University of Washington; Ryan Calo, School of Law, University of Washington; Franziska Roesner, Paul G. Allen School of Computer Science & Engineering, University of Washington; Tadayoshi Kohno, Paul G. Allen School of Computer Science & Engineering, University of Washington. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proit or commercial advantage and that copies bear this notice and the full citation on the irst page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior speciic permission and/or a fee. Request permissions from permissions@acm.org. © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM. 2576-5337/2022/7-ART $15.00 https://doi.org/10.1145/3480464 Digit. Threat. Res. Pract.