Data for training learning-enabled self-driving cars in the physical world are typically collected in a safe, normal environment. Such data distribution often engenders a strong bias towards safe driving, making self-driving cars unprepared when encountering adversarial scenarios like unexpected accidents. Due to a dearth of such adverse data that is unrealistic for drivers to collect, autonomous vehicles can perform poorly when experiencing such rare events. This work addresses much-needed research by having participants drive a VR vehicle simulator going through simulated traffic with various types of accidental scenarios. It aims to understand human responses and behaviors in simulated accidents, contributing to our understanding of driving dynamics and safety. The simulation framework adopts a robust traffic simulation and is rendered using the Unity Game Engine. Furthermore, the simulation framework is built with portable, light-weight immersive driving simulator hardware, lowering the resource barrier for studies in autonomous driving research.

One of the biggest privacy concerns of smart home users is enforcing limits on household members’ access to devices and each other’s data. While people commonly express preferences for intricate access control policies, in practice they often settle for less secure defaults. As an alternative, this paper investigates "optimistic access control" policies that allow users to obtain access and data without pre-approval, subject to oversight from other household members. This solution allows users to leverage the interpersonal trust they already rely on in order to establish privacy boundaries commensurate with more complex access control methods, while retaining the convenience of less secure strategies. To evaluate this concept, we conducted a series of surveys with 604 people total, studying the acceptability and perceptions of this approach. We found that a number of respondents preferred optimistic modes to existing access control methods and that interest in optimistic access varied with device type and household characteristics.

In the United States, items in police possession are often sold at auction if they are not claimed. This includes cellphones that the police obtained through civil asset forfeiture, that were stolen, or that were turned in to lost-and-found. Thousands of US police departments partner with a website, PropertyRoom, to auction their items. Over the course of several months, we purchased 228 cellphones from PropertyRoom to ascertain whether they contained personal information. Our results show that a shocking amount of sensitive, personal information is easily accessible, even to a "low-effort" adversary with no forensics expertise: 21.5% of the phones we purchased were not locked at all, another 4.8% used top-40 most common PINs and patterns, and one phone had a sticky-note from the police with the PIN on it. We analyze the content on the 61 phones we could access, finding sensitive information about not only the phones’ previous owners, but also about their personal contacts, and in some cases, about victims of those persons’ crimes. Additionally, we analyze approximately two years of PropertyRoom cellphone auctions, finding multiple instances of identifying information in photos of the items being auctioned, including sticky-notes with PINs, owners’ names and phone numbers, and evidence stickers that reveal how the phones were obtained and the names of the officers who obtained them. Our work shows that police procedures and phone auctions can be a significant source of personal information leakage and re-victimization. We hope that our work is a call to arms to enforce new policies that either prohibit the selling of computing devices containing user information, or at the very least impose requirements to wipe phones in a manner that the US federal government already employs.

Intimate partner violence (IPV) is a pervasive societal problem that affects millions of people around the world. IPV perpetrators increasingly weaponize digital technologies like mobile applications (“apps”) to spy on, monitor, and harass victims. Surveillance-capable apps can have legitimate use cases, for example, locating children, and are therefore easily available on various mobile app stores like the Google Play Store. Nevertheless, these applications are easily repurposed by abusers to track their victims. The problem of such dual-use apps in IPV is global. However, current understanding of the ecosystem of such apps is limited to English-language apps, potentially limiting its relevance to non-English speaking IPV survivors across the world. In this paper, we study the prevalence of dualuse applications found in 15 languages and 27 countries. We collected 51,868 unique apps in 2020 from the Google Play Store, using queries such as “track wife’s location.” Through a semi-manual analysis of a subset of these apps, we discovered 854 unique dualuse apps, and estimate that among the apps collected from Google Play, 3,988 are dual-use apps. We found notable differences in app search results, suggested queries, and marketed capabilities of dual-use apps across different languages. For instance, we identified that 18% of dual-use apps do not have an English description, and 28% could not be found using English queries. Google Play (cursorily) blocks certain queries referring explicitly to intimate partner surveillance (IPS) to discourage potential abusers, but the blocking efficacy varies across languages. For example, we found that 80% of explicit IPS queries for English are blocked, but none for Bengali, Chinese, Hindi, Malay, Thai, and Vietnamese. Thus, abusers fluent in those languages can evade such blocking with no effort.