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Multi-Factor Authentication is intended to strengthen the security of password-based authentication by adding another factor, such as hardware tokens or one-time passwords using mobile apps. However, this increased authentication security comes with potential drawbacks that can lead to account and asset loss. If users lose access to their additional authentication factors for any reason, they will be locked out of their accounts. Consequently, services that provide Multi-Factor Authentication should deploy procedures to allow their users to recover from losing access to their additional factor that are both secure and easy-to-use. In this work, we investigate the security and user experience of Multi-Factor Authentication recovery procedures, and compare their deployment to descriptions on help and support pages. We first evaluate the official help and support pages of 1,303 websites that provide Multi-Factor Authentication and collect documented information about their recovery procedures. Second, we select a subset of 71 websites, create accounts, set up Multi-Factor Authentication, and perform an in-depth investigation of their recovery procedure security and user experience. We find that many websites deploy insecure Multi-Factor Authentication recovery procedures and allowed us to circumvent and disable Multi-Factor Authentication when having access to the accounts' associated email addresses. Furthermore, we commonly observed discrepancies between our in-depth analysis and the official help and support pages, implying that information meant to aid users is often either incorrect or outdated. Based on our findings, we provide recommendations for best practices regarding Multi-Factor Authentication recovery.

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 "close contacts" and identify prior contacts in the event an individual tests positive. However, there has been significant public discussion about the tensions between effective 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 first surveys were on April 1 and 3, before the first 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 reflect 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. We are continuing to conduct longitudinal measurements and will update this report over time; citations to this version of the report should reference Report Version 2.0, December 4, 2020., Comment: 37 pages, 11 figures. Supercedes arXiv:2005.06056

There is growing interest in 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 recognize the potential for smartphones, IoT devices, and wearable technology to automatically track "close contacts" and identify prior contacts in the event of an individual's positive test. However, there is currently significant public discussion about the tensions between effective technology-based contact tracing and the privacy of individuals. To inform this discussion, we present the results of a sequence of online surveys focused on contact tracing and privacy, each with 100 participants. Our first surveys were on April 1 and 3, and we report primarily on those first two surveys, though we present initial findings from later survey dates as well. Our results present the diversity of public opinion and can inform the public discussion on whether and how to leverage technology to reduce the spread of COVID-19. We are continuing to conduct longitudinal measurements, and will update this report over time; citations to this version of the report should reference Report Version 1.0, May 8, 2020. NOTE: As of December 4, 2020, this report has been superseded by Report Version 2.0, found at arXiv:2012.01553. Please read and cite Report Version 2.0 instead., Comment: 32 pages, 10 figures (including 5 in appendix B), 1 table, 2 appendices. NOTE: As of December 4, 2020, this report has been superseded by Report Version 2.0,found at arXiv:2012.01553. Please read and cite Report Version 2.0 instead

Technology is increasingly used -- unintentionally (misinformation) or intentionally (disinformation) -- to spread false information at scale, with potentially broad-reaching societal effects. For example, technology enables increasingly realistic false images and videos, and hyper-personal targeting means different people may see different versions of reality. This report is the culmination of a PhD-level special topics course (https://courses.cs.washington.edu/courses/cse599b/18au/) in Computer Science & Engineering at the University of Washington's Paul G. Allen School in the fall of 2018. The goals of this course were to study (1) how technologies and today's technical platforms enable and support the creation and spread of such mis- and disinformation, as well as (2) how technical approaches could be used to mitigate these issues. In this report, we summarize the space of technology-enabled mis- and disinformation based on our investigations, and then surface our lessons and recommendations for technologists, researchers, platform designers, policymakers, and users.