As the number of smart devices increases in our lives, the data they collect to perform valuable tasks, such as voice assistant requests, comes at the cost of user privacy. To mitigate their privacy impact, emerging usable privacy-aware sensing (UPAS) research has relied on cross-disciplinary approaches that extend past the core focus of broader academic research communities, such as Security & Privacy or Human-Computer Interaction. These works incorporate privacy design principles, whereby systems include safeguards by combining usable privacy (UP) with privacy-aware sensing (PAS) design to protect users' privacy. To better understand this emerging area of research, we conducted a mixed qualitative and quantitative Systematization of Knowledge (SoK). With a thorough review of pertinent literature, resulting in 114 selected works (reduced from 10,122 across 12 venues), we found that, despite the similarity of these works, many are dispersed across multiple communities, utilize community-specific jargon and keywords, and minimally overlap in design and evaluation approaches, potentially hindering cross-pollination across communities and thereby slowing the growth of this emerging research area. Thus, these factors helped reveal a research gap in this space. We use these findings to present four research themes and provide community and design recommendations to encourage cross-disciplinary UPAS research.

The overturn of Roe v. Wade has taken away the constitutional right to abortion. Prior work shows that period-tracking apps’ data practices can be used to detect pregnancy and abortion, hence putting women at risk of being prosecuted. It is unclear how much women know about the privacy practices of such apps and how concerned they are after the overturn. Such knowledge is critical to designing effective strategies for stakeholders to enhance women’s reproductive privacy. We conducted an online 183-participant vignette survey with US women from states with diverse policies on abortion. Participants were significantly concerned about the privacy practices of the period-tracking apps, such as data access by law enforcement and third parties. However, participants felt uninformed and powerless about risk mitigation practices. We provide several recommendations to enhance women’s privacy awareness toward their period-tracking practices.

Recent years have seen a sharp increase in the number of underage users in virtual reality (VR), where security and privacy (S&P) risks such as data surveillance and self-disclosure in social interaction have been increasingly prominent. Prior work shows children largely rely on parents to mitigate S&P risks in their technology use. Therefore, understanding parents’ S&P knowledge, perceptions, and practices is critical for identifying the gaps for parents, technology designers, and policymakers to enhance children’s S&P. While such empirical knowledge is substantial in other consumer technologies, it remains largely unknown in the context of VR. To address the gap, we conducted in-depth semi-structured interviews with 20 parents of children under the age of 18 who use VR at home. Our findings highlight parents generally lack S&P awareness due to the perception that VR is still in its infancy. To protect their children’s interactions with VR, parents currently primarily rely on active strategies such as verbal education about S&P. Passive strategies such as using parental controls in VR are not commonly used among our interviewees, mainly due to their perceived technical constraints. Parents also highlight that a multi-stakeholder ecosystem must be established towards more S&P support for children in VR. Based on the findings, we propose actionable S&P recommendations for critical stakeholders, including parents, educators, VR companies, and governments.

Designing privacy-respecting and human-centric smart cities requires a careful investigation of people’s attitudes and concerns toward city-wide data collection scenarios. To capture a holistic view, we carried out this investigation in two phases. We first surfaced people’s understanding, concerns, and expectations toward smart city scenarios by conducting 21 semi-structured interviews with people in underserved communities. We complemented this in-depth qualitative study with a 348-participant online survey of the general population to quantify the significance of smart city factors (e.g., type of collected data) on attitudes and concerns. Depending on demographics, privacy and ethics were the two most common types of concerns among participants. We found the type of collected data to have the most and the retention time to have the least impact on participants’ perceptions and concerns about smart cities. We highlight key takeaways and recommendations for city stakeholders to consider when designing inclusive and protective smart cities.

Gender stereotypes remain common in U.S. society and harm people of all genders. Focusing on binary genders (women and men) as a first investigation, we empirically study gender stereotypes related to computer security and privacy. We used Prolific to conduct two surveys with U.S. participants that aimed to: (1) surface potential gender stereotypes related to security and privacy (N = 202), and (2) assess belief in gender stereotypes about security and privacy engagement, personal characteristics, and behaviors (N = 190). We find that stereotype beliefs are significantly correlated with participants’ gender as well as level of sexism, and we delve into the justifications our participants offered for their beliefs. Beyond scientifically studying the existence and prevalence of such stereotypes, we describe potential implications, including biasing crowdworker-faciliated user research. Further, our work lays a foundation for deeper investigations of the impacts of stereotypes in computer security and privacy, as well as stereotypes across the whole gender and identity spectrum.

Internet-connected consumer devices have rapidly increased in popularity; however, relatively little is known about how these technologies are affecting interpersonal relationships in multi-occupant households. In this study, we conduct 13 semi-structured interviews and survey 508 individuals from a variety of backgrounds to discover and categorize how consumer IoT devices are affecting interpersonal relationships in the United States. We highlight several themes, providing exploratory data about the pervasiveness of interpersonal costs and benefits of consumer IoT devices. These results inform follow-up studies and design priorities for future IoT technologies to amplify positive and reduce negative interpersonal effects.

In prior work, researchers proposed an Internet of Things (IoT) security and privacy label akin to a food nutrition label, based on input from experts. We conducted a survey with 1,371 Mechanical Turk (MTurk) participants to test the effectiveness of each of the privacy and security attribute-value pairs proposed in that prior work along two key dimensions: ability to convey risk to consumers and impact on their willingness to purchase an IoT device. We found that the values intended to communicate increased risk were generally perceived that way by participants. For example, we found that consumers perceived more risk when a label conveyed that data would be sold to third parties than when it would not be sold at all, and that consumers were more willing to purchase devices when they knew that their data would not be retained or shared with others. However, participants’ risk perception did not always align with their willingness to purchase, sometimes due to usability concerns. Based on our findings, we propose actionable recommendations on how to more effectively present privacy and security attributes on an IoT label to better communicate risk to consumers.

Information about the privacy and security of Internet of Things (IoT) devices is not readily available to consumers who want to consider it before making purchase decisions. While legislators have proposed adding succinct, consumer accessible, labels, they do not provide guidance on the content of these labels. In this paper, we report on the results of a series of interviews and surveys with privacy and security experts, as well as consumers, where we explore and test the design space of the content to include on an IoT privacy and security label. We conduct an expert elicitation study by following a three-round Delphi process with 22 privacy and security experts to identify the factors that experts believed are important for consumers when comparing the privacy and security of IoT devices to inform their purchase decisions. Based on how critical experts believed each factor is in conveying risk to consumers, we distributed these factors across two layers-a primary layer to display on the product package itself or prominently on a website, and a secondary layer available online through a web link or a QR code. We report on the experts' rationale and arguments used to support their choice of factors. Moreover, to study how consumers would perceive the privacy and security information specified by experts, we conducted a series of semi-structured interviews with 15 participants, who had purchased at least one IoT device (smart home device or wearable). Based on the results of our expert elicitation and consumer studies, we propose a prototype privacy and security label to help consumers make more informed IoT-related purchase decisions.

Despite growing concerns about security and privacy of Internet of Things (IoT) devices, consumers generally do not have access to security and privacy information when purchasing these devices. We interviewed 24 participants about IoT devices they purchased. While most had not considered privacy and security prior to purchase, they reported becoming concerned later due to media reports, opinions shared by friends, or observing unexpected device behavior. Those who sought privacy and security information before purchase, reported that it was difficult or impossible to find. We asked interviewees to rank factors they would consider when purchasing IoT devices; after features and price, privacy and security were ranked among the most important. Finally, we showed interviewees our prototype privacy and security label. Almost all found it to be accessible and useful, encouraging them to incorporate privacy and security in their IoT purchase decisions.

As increasingly many Internet-of-Things (IoT) devices collect personal data, users face more privacy decisions. Personal privacy assistants can provide social cues and help users make informed decisions by presenting information about how others have decided in similar cases. To better understand which social cues are relevant and whose recommendations users are more likely to follow, we presented 1000 online participants with nine IoT data-collection scenarios. Some participants were told the percentage of experts or friends who allowed data collection in each scenario, while other participants were provided no social cue. At the conclusion of each scenario, participants were asked whether they would allow the described data collection. Our results help explain under what circumstances users are more or less likely to be swayed by the reported behavior of others in similar scenarios. For example, our results indicate that when friends denied data collection, our participants were more influenced than when friends allowed data collection. On the other hand, participants were more influenced by experts when they allowed data collection. We also observed that influence could get stronger or wear off when participants were exposed to a sequence of scenarios. For example, when experts and friends repeatedly allowed data collection in scenarios with clear risk or denied it in scenarios with clear benefits, participants were less likely to be influenced by them in subsequent scenarios.

Text passwords---a frequent vector for account compromise, yet still ubiquitous---have been studied for decades by researchers attempting to determine how to coerce users to create passwords that are hard for attackers to guess but still easy for users to type and memorize. Most studies examine one password or a small number of passwords per user, and studies often rely on passwords created solely for the purpose of the study or on passwords protecting low-value accounts. These limitations severely constrain our understanding of password security in practice, including the extent and nature of password reuse, password behaviors specific to categories of accounts (e.g., financial websites), and the effect of password managers and other privacy tools.

Despite their ubiquity, many password meters provide inaccurate strength estimates. Furthermore, they do not explain to users what is wrong with their password or how to improve it. We describe the development and evaluation of a data-driven password meter that provides accurate strength measurement and actionable, detailed feedback to users. This meter combines neural networks and numerous carefully combined heuristics to score passwords and generate data-driven text feedback about the user's password. We describe the meter's iterative development and final design. We detail the security and usability impact of the meter's design dimensions, examined through a 4,509-participant online study. Under the more common password-composition policy we tested, we found that the data-driven meter with detailed feedback led users to create more secure, and no less memorable, passwords than a meter with only a bar as a strength indicator.