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.

In this work, we consider the computer security and privacy practices and needs of recently resettled refugees in the United States. We ask: How do refugees use and rely on technology as they settle in the US? What computer security and privacy practices do they have, and what barriers do they face that may put them at risk? And how are their computer security mental models and practices shaped by the advice they receive?We study these questions through in-depth qualitative interviews with case managers and teachers who work with refugees at a local NGO, as well as through focus groups with refugees themselves. We find that refugees must rely heavily on technology (e.g., email) as they attempt to establish their lives and find jobs; that they also rely heavily on their case managers and teachers for help with those technologies; and that these pressures can push security practices into the background or make common security "best practices" infeasible. At the same time, we identify fundamental challenges to computer security and privacy for refugees, including barriers due to limited technical expertise, language skills, and cultural knowledge -for example, we find that scams as a threat are a new concept for many of the refugees we studied, and that many common security practices (e.g., password creation techniques and security questions) rely on US cultural knowledge. From these and other findings, we distill recommendations for the computer security community to better serve the computer security and privacy needs and constraints of refugees, a potentially vulnerable population that has not been previously studied in this context.

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Source code attribution classifiers have recently become powerful. We consider the possibility that an adversary could craft code with the intention of causing a misclassification, i.e., creating a forgery of another author's programming style in order to hide the forger's own identity or blame the other author. We find that it is possible for a non-expert adversary to defeat such a system. In order to inform the design of adversarially resistant source code attribution classifiers, we conduct two studies with C/C++ programmers to explore the potential tactics and capabilities both of such adversaries and, conversely, of human analysts doing source code authorship attribution. Through the quantitative and qualitative analysis of these studies, we (1) evaluate a state-of-the-art machine classifier against forgeries, (2) evaluate programmers as human analysts/forgery detectors, and (3) compile a set of modifications made to create forgeries. Based on our analyses, we then suggest features that future source code attribution systems might incorporate in order to be adversarially resistant.

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