filtering snowball sampling search results final sample Figure 1: We searched for publications on tangible privacy and security interfaces (TaPSI) in 28 usable privacy and security (UPS)venues and used snowball sampling to broaden our sample further.Applying hybrid thematic analysis to our final sample (𝑛 = 80), we describe the used terminology and definitions, addressed UPS domains, contributions, methods, implementations, and opportunities or challenges inherent to TaPSI.Based on these findings, we present the TaPSI Research Framework, which gives recommendations for future researchers and describes a design space for TaPSI.

Figure 1: Our study explores user perceptions of web-browser tracking protection plugins under the three conditions no plugin, functional plugin, and placebo plugin, where users were only primed with narratives without altering the website.During a hotel booking task, users felt more protected with functional or placebo plugins despite no actual changes to the website, revealing the participant's inability to judge tracking protection effectiveness accurately.We generated the figure using DALL-E.

Today’s context-aware mobile phones allow developers to build intelligent and adaptive applications. The data demand induced by context awareness leads to decreased trust and increased privacy concerns. However, users’ deeper reasons and real-world fears that underlie these concerns are not fully understood. We conducted an online survey (N=100) and semi-structured interviews (N=20) to understand users’ concerns about smartphone data privacy. We investigated three key areas: general user understanding and misconceptions, specific in-depth concerns, and mitigation strategies. We found that effective transparency and control are the central themes across all areas. Users are concerned about privacy issues negatively impacting their lives, especially through financial loss, physical harm, or manipulation. We show that privacy measures should be implemented with a stronger focus on the user by keeping the user in the loop through transparency and control.

Labels inform smart home users about the privacy of devices before purchase and during use. Yet, current privacy labels fail to fully reflect the impact of advanced device configuration options like sensor state control. Based on the successful implementation of related privacy and security labels, we designed extended static and interactive labels that reflect sensor states and device connectivity. We first did expert interviews (N = 10) that informed the final label design. Second, we ran an online survey (N = 160) to assess the interpretation and usability of the novel interactive privacy label. Lastly, we conducted a second survey (N = 120) to investigate how well our interactive labels educate users about sensor configuration. We found that most participants successfully used the interactive label and retrieved sensor information more efficiently and correctly. We discuss our findings in the context of a potential shift in label use toward control and use-case-based interaction.

Captions are a valuable scaffold for language learners, aiding comprehension and vocabulary acquisition.Past work has proposed enhancements such as keyword highlights for increased learning gains.However, little is known about learners' experience with enhanced captions, although this is critical for adoption in everyday life.We conducted a survey and focus group to elicit learner preferences and requirements and implemented a processing pipeline CCS Concepts• Applied

Users frequently use their smartphones in combination with other smart devices, for example, when streaming music to smart speakers or controlling smart appliances. During these interconnected interactions, user data gets handled and processed by several entities that employ different data protection practices or are subject to different regulations. Users need to understand these processes to inform themselves in the right places and make informed privacy decisions. We conducted an online survey (N=120) to investigate whether users have accurate mental models about interconnected interactions. We found that users consider scenarios more privacy-concerning when multiple devices are involved. Yet, we also found that most users do not fully comprehend the privacy-relevant processes in interconnected interactions. Our results show that current privacy information methods are insufficient and that users must be better educated to make informed privacy decisions. Finally, we advocate for restricting data processing to the app layer and better encryption to reduce users' data protection responsibilities.

Images and videos are widely used to elicit emotions; however, their visual appeal differs from real-world experiences. With virtual reality becoming more realistic, immersive, and interactive, we envision virtual environments to elicit emotions effectively, rapidly, and with high ecological validity. This work presents the first interactive virtual reality dataset to elicit emotions. We created five interactive virtual environments based on corresponding validated 360° videos and validated their effectiveness with 160 participants. Our results show that our virtual environments successfully elicit targeted emotions. Compared with the existing methods using images or videos, our dataset allows virtual reality researchers and practitioners to integrate their designs effectively with emotion elicitation settings in an immersive and interactive way.

Most smart home devices have multiple sensors, such as cameras and microphones; however, most cannot be controlled individually. Tangible privacy mechanisms provide control over individual sensors and instill high certainty of privacy. Yet, it remains unclear how they can be used in future smart homes. We conducted three studies to understand how tangible privacy mechanisms scale across multiple devices and respond to user needs. First, we conducted a focus group (N=8) on speculative tangible control artifacts to understand the user perspective. Second, we ran a workshop at a human-computer interaction conference (N=8) on tangible privacy. Third, we conducted a six-week in-the-wild study with a tangible, static privacy dashboard across six households. Our findings help to contrast the need for tangible privacy mechanisms on the sensor level with user needs on a smart home level. Finally, we discuss our design implications for future smart homes through the lens of inclusive privacy.

We are constantly surrounded by technology that collects and processes sensitive data, paving the way for privacy violations. Yet, current research investigating technology-facilitated privacy violations in the physical world is scattered and focused on specific scenarios or investigates such violations purely from an expert’s perspective. Informed through a large-scale online survey, we first construct a scenario taxonomy based on user-experienced privacy violations in the physical world through technology. We then validate our taxonomy and establish mitigation strategies using interviews and co-design sessions with privacy and security experts. In summary, this work contributes (1) a refined scenario taxonomy for technology-facilitated privacy violations in the physical world, (2) an understanding of how privacy violations manifest in the physical world, (3) a decision tree on how to inform users, and (4) a design space to create notices whenever adequate. With this, we contribute a conceptual framework to enable a privacy-preserving technology-connected world.

While systems that use Artificial Intelligence (AI) are increasingly becoming part of everyday technology use, we do not fully understand how AI changes design processes. A structured understanding of how designers work with AI is needed to improve the design process and educate future designers. To that end, we conducted interviews with designers who participated in projects which used AI. While past work focused on AI systems created by experienced designers, we focus on the perspectives of a diverse sample of interaction designers. Our results show that the design process of an interactive system is affected when AI is integrated and that design teams adapt their processes to accommodate AI. Based on our data, we contribute four approaches adopted by interaction designers working with AI: a priori, post-hoc, model-centric, and competence-centric. Our work contributes a pragmatic account of how design processes for AI systems are enacted.

Users avoid engaging with privacy policies because they are lengthy and complex, making it challenging to retrieve relevant information. In response, research proposed contextual privacy policies (CPPs) that embed relevant privacy information directly into their affiliated contexts. To date, CPPs are limited to concept showcases. This work evolves CPPs into a production tool that automatically extracts and displays concise policy information. We first evaluated the technical functionality on the US’s 500 most visited websites with 59 participants. Based on our results, we further revised the tool to deploy it in the wild with 11 participants over ten days. We found that our tool is effective at embedding CPP information on websites. Moreover, we found that the tool’s usage led to more reflective privacy behavior, making CPPs powerful in helping users understand the consequences of their online activities. We contribute design implications around CPP presentation to inform future systems design.

Privacy policies (PPs) are currently the only way to inform users about their rights and choices during web browsing and searching. However, users avoid engaging with them, because of their length and abstract legal language, which makes them hard to read and understand. We propose to support the understanding of PPs by using modular icons. Icons have already proven to be helpful in visualizing concepts with high information density. However, the value of using icons to supplement PPs lacks a scientific foundation. Thus, we conducted two studies to evaluate existing icon sets for their understandability and to teach participants their meaning in situ. We show that modular privacy icons can be taught using our process, which has the potential to aid quicker and easier comprehension of PPs. We contribute a set of tested modular privacy icons and a verified process on how to teach them to users incidentally during web browsing.

Today, adaptive mobile applications use mobile sensing and user tracking, allowing for adaptation to the users' context and needs. This raises several privacy concerns. Privacy dashboards provide transparency and sharing control; however, their impact on the users' behavior is unclear. To shed light on the effects of (a) transparency and (b) control features, we developed a mobile sensing privacy dashboard and evaluated it in the wild (N=227). We found that the pure presentation of raw logging data is rather deterring, and users tend to use the app less, but offering the user control over the data collection can compensate for that. Users used the control features rarely and, as such, did not affect the data collocation. Our work informs the design of future privacy-enhancing interfaces in applications relying on passively collected mobile sensing data. Moreover, our results encourage the adoption of privacy dashboards in the applications and relieve developers from concerns about the negative influences of transparency information on the quality of collected data.

Private homes are increasingly becoming smart spaces. While smart homes promise comfort, they expose most intimate spaces to security and privacy risks. Unfortunately, most users today are not equipped with the right tools to assess the vulnerabilities or privacy practices of smart devices. Further, users might lose track of the devices installed in their homes or are unaware of devices placed by a partner or host. We developed SaferHome, an interactive digital-physical privacy framework, to provide smart home users with security and privacy assessments and a sense of device location. SaferHome includes a digital list view and physical and digital dashboards that map real floor plans. We evaluated SaferHome with eight households in the wild. We find that users adopted various strategies to integrate the dashboards into their understanding and interpretation of smart home privacy. We present implications for the design of future smart home privacy frameworks that are impacted by technical affinity, device types, device ownership, and tangibility of assessments.

As ubiquitous computing brings sensors and actuators directly into our homes, they introduce privacy concerns for the owners and bystanders. However, privacy concerns may vary among devices and depend on the bystanders' social relation to the owner. In this work, we hypothesize 1) that bystanders assign more privacy concerns to smart home devices than personal computing devices, such as smartphones, even though they have the same capabilities, and 2) that a stronger social relationship mitigates some of the bystanders' privacy concerns. By conducting an online survey (n=170), we found that personal computing devices are perceived as significantly less privacy-concerning than smart home devices while having equal capabilities. By varying the assumed social relationship, we further found that a stronger connection to the owner reduces privacy concerns. Thus, as bystanders underestimate the risk of personal computing devices and are generally concerned about smart home devices, it is essential to alert the user about the presence of both. We argue that bystanders have to be informed about the privacy risks while entering a new space, in the best case, already in the entrance area.

Online services collect an increasing amount of data about their users. Privacy policies are currently the only common way to inform users about the kinds of data collected, stored and processed by online services. Previous work showed that users do not read and understand privacy policies, due to their length, difficult language, and often non-prominent location. Embedding privacy-relevant information directly in the context of use could help users understand the privacy implications of using online services. We implemented Contextual Privacy Policies (CPPs) as a browser extension and provide it to the community to make privacy information accessible for end-users. We evaluated CPPs through a one-week deployment and in situ questionnaires as well as pre- and post-study interviews. We found that CPPs were well received by participants. The analysis revealed that provided information should be as compact as possible, be adjusted to user groups and enable users to take action.