Although older adults are increasingly adopting digital social technologies, a lack of knowledge and experience makes them vulnerable to digital privacy and security threats. It is, therefore, crucial to build digital privacy education interventions that empower older adults to take more control over their digital privacy. Most tutorials and support materials are designed for the younger generations and are not necessarily as effective for the older population. In this paper, we explore the development of education interventions suited to the learning styles of the older adult population. We particularly develop interventions that span a variety of modalities (text, videos, audio presentations, infographics, comics, interactive tutorials, and chatbots) and evaluate these interventions in a focus group study, gathering feedback from both older and younger adults regarding the education interventions and how to improve them. Our findings demonstrate that there are distinct differences in modality preferences between older and younger adults. In this paper, we discuss our findings and contribute to the development of digital privacy education interventions that are tailored to the specific needs and preferences of older adults.

Teammates powered by artificial intelligence (AI) are becoming more prevalent and capable in their abilities as a teammate. While these teammates have great potential in improving team performance, empirical work that explores the impacts of these teammates on the humans they work with is still in its infancy. Thus, this study explores how the inclusion of AI teammates impacts both the performative abilities of human-AI teams in addition to the perceptions those humans form. The current study found that participants perceiving their third teammate as artificial performed worse than those perceiving them as human. Furthermore, these performance differences were significantly moderated by the task’s difficulty, with participants in the AI teammate condition significantly outperforming participants perceiving a human teammate in the highest difficulty task, which diverges from previous human-AI teaming literature. Alternatively, no significant effect of perceived teammate artificiality was found on shared mental model similarity. However, it did significantly affect participants’ levels of perceived team cognition. Individual performance on medium difficulty maps also mediated the effect of perceived teammate artificiality on perceived team cognition. These results further build on the current understanding of how AI teammates impact perceptions of individual human teammates and how those perceptions subsequently impact their objective performance, which is critical in building more effective AI teammates to incorporate alongside humans.

One widespread, but frequently overlooked, source of security information is influencer marketing ads on YouTube for security and privacy products such as VPNs. This paper examines how widespread these ads are, where on YouTube they are found, and what kind of information they convey. Starting from a random sample of 1.4% of YouTube, we identify 243 videos containing VPN ads with a total of 63 million views. Using qualitative analysis, we find that these ads commonly discuss broad security guarantees as well as specific technical features, frequently focus on internet threats, and sometimes emphasize accessing otherwise unavailable content. Different VPN companies tend to advertise in different categories of channels and emphasize different messages. We find a number of potentially misleading claims, including overpromises and exaggerations that could negatively influence viewers’ mental models of internet safety.

Abstract Research finds that the users of Social Networking Sites (SNSs) often fail to comprehensively engage with the plethora of available privacy features— arguably due to their sheer number and the fact that they are often hidden from sight. As different users are likely interested in engaging with different subsets of privacy features, an SNS could improve privacy management practices by adapting its interface in a way that proactively assists, guides, or prompts users to engage with the subset of privacy features they are most likely to benefit from. Whereas recent work presents algorithmic implementations of such privacy adaptation methods, this study investigates the optimal user interface mechanism to present such adaptations. In particular, we tested three proposed“adaptation methods”(automation, suggestions, highlights) in an online between-subjects user experiment in which 406 participants used a carefully controlled SNS prototype. We systematically evaluate the effect of these adaptation methods on participants’ engagement with the privacy features, their tendency to set stricter settings (protection), and their subjective evaluation of the assigned adaptation method. We find that theautomationof privacy features afforded users the most privacy protection, while giving privacysuggestionscaused the highest level of engagement with the features and the highest subjective ratings (as long as awkward suggestions are avoided). We discuss the practical implications of these findings in the effectiveness of adaptations improving user awareness of, and engagement with, privacy features on social media.

The COVID-19 pandemic lockdown lead to the rapid adoption and use of various groupware applications ("apps'') for remote connection with colleagues, friends, and family. Different factors such as user experiences, trust, and social influences ("user-situational motivations'') were instrumental in determining how and what apps people adopted and used, especially at the onset of the COVID-19 pandemic. In this empirical study, we examine how these factors and four predominant user-situational motivations (i.e., the mandated use of an app by an employer/institution, recommended use of an app by an employer/institution, recommended use of an app by a peer(s), and self-selection of an app) influenced the rapid adoption and use of groupware applications. Specifically, we develop an "emergency adoption model" of groupware applications using 195 valid survey responses to highlight the factors that motivated these apps' use at the onset of COVID-19 pandemic lockdown. We leverage the Technology Adoption Model (TAM) and integrate it with the users' past use of the application before the COVID-19 lockdown, user-situational motivation, and their privacy-related trust in the application provider to develop a more comprehensive model. Using confirmatory factor analysis (CFA) and structural equation modeling (SEM), we find that the users who used a groupware app in the past continued to use it, and in line with TAM, users' intention to adopt and use a groupware application was largely driven by the ease-of-use and usefulness of the app. Furthermore, while not a part of the traditional TAM model, we find that privacy-related trust in the application provider plays an important role in emergency adoption. However, unlike typical adoption models, the nature of all these effects---most prominently those related to privacy-related trust---depend on the underlying situational motivation. We discuss the implications of these findings and suggest ways to improve the adoption and use of groupware applications, especially during crises like the COVID-19 pandemic.

Abstract Virtual Private Networks (VPNs) can help people protect their privacy. Despite this, VPNs are not widely used among the public. In this survey study about the adoption and usage of VPNs, we investigate people’s motivation to use VPNs and the barriers they encounter in adopting them. Using data from 90 technologically savvy participants, we find that while nearly all (98%; 88) of the participants have knowledge about what VPNs are, less than half (42%; 37) have ever used VPNs primarily as a privacy-enhancing technology. Of these, 18% (7) abandoned using VPNs while 81% (30) continue to use them to protect their privacy online. In a qualitative analysis of survey responses, we find that people who adopt and continue to use VPNs for privacy purposes are primarily motivated by emotional considerations, including the strong desire to protect their privacy online, wide fear of surveillance and data tracking not only from Internet service providers (ISPs) but also governments and Internet corporations such as Facebook and Google. In contrast, people who are mainly motivated by practical considerations are more likely to abandon VPNs, especially once their practical need no longer exists. These people cite their access to alternative technologies and the effort required to use a VPN as reasons for abandonment. We discuss implications of these findings and provide suggestions on how to maximize adoption of privacy-enhancing technologies such as VPNs, focusing on how to align them with people’s interests and privacy risk evaluation.

Abstract Smartphone users are often unaware of mobile applications’ (“apps”) third-party data collection and sharing practices, which put them at higher risk of privacy breaches. One way to raise awareness of these practices is by providing unobtrusive but pervasive visualizations that can be presented in a glanceable manner. In this paper, we applied Wogalter et al.’s Communication-Human Information Processing model (C-HIP) to design and prototype eight different visualizations that depict smartphone apps’ data sharing activities. We varied the granularity and type (i.e., data-centric or app-centric) of information shown to users and used the screensaver/lock screen as a design probe. Through interview-based design probes with Android users (n=15), we investigated the aspects of the data exposure visualizations that influenced users’ comprehension and privacy awareness. Our results shed light on how users’ perceptions of privacy boundaries influence their preference regarding the information structure of these visualizations, and the tensions that exist in these visualizations between glanceability and granularity. We discuss how a pervasive, soft paternalistic approach to privacy-related visualization may raise awareness by enhancing the transparency of information flow, thereby, unobtrusively increasing users’ understanding of data sharing practices of mobile apps. We also discuss implications for privacy research and glanceable security.