Vocal training is difficult because the muscles that control pitch, resonance, and phonation are internal and invisible to learners. This paper investigates how Electromyography (EMG) and ultrasonic imaging (UI) can make these muscles observable for training purposes. We report three studies. First, we analyze the EMG and UI data from 16 singers (beginners, experienced professionals), revealing differences among three vocal groups of the muscle control proficiency. Second, we use the collected data to create a system that visualizes an expert's muscle activity as reference. This system is tested in a user study with 12 novices, showing that EMG highlighted muscle activation nuances, while UI provided insights into vocal cord length and dynamics. Third, to compare our approach to traditional methods (audio analysis and coach instructions), we conducted a focus group study with 15 experienced singers. Our results suggest that EMG is promising for improving vocal skill development and enhancing feedback systems. We conclude the paper with a detailed comparison of the analyzed modalities (EMG, UI and traditional methods), resulting in recommendations to improve vocal muscle training systems.

While recent usable privacy and security (UPS) research has made progress in moving beyond “the average user,” a systematic account of how UPS researchers navigate diversity and inclusion in their work remains lacking. Through 20 in-depth semi-structured interviews with experienced researchers, we examine how and why they recruit diverse, underserved populations in their work, as well as the challenges they face in doing so, including conceptual difficulties in defining who is underserved, limited access to target populations, and inflexible peer review and publishing norms. Participants also reflected on their own positionality when planning and conducting studies, often expressing uncertainty about how to account for and articulate their positionality. We identify strategies researchers use to overcome challenges and highlight areas where collective action from the research community and institutions is needed to foster greater inclusion in UPS research practices.

This paper presents a comprehensive Systematization of Knowledge on tangible privacy and security interfaces (TaPSI). Tangible interfaces provide physical forms for digital interactions. They can offer significant benefits for privacy and security applications by making complex and abstract security concepts more intuitive, comprehensible, and engaging. Through a literature survey, we collected and analyzed 80 publications. We identified terminology used in these publications and addressed usable privacy and security domains, contributions, applied methods, implementation details, and opportunities or challenges inherent to TaPSI. Based on our findings, we define TaPSI and propose the TaPSI Research Framework, which guides future research by offering insights into when and how to conduct research on privacy and security involving TaPSI as well as a design space of TaPSI.

This paper contributes an in-depth investigation (N=24) of privacy perceptions in the context of medical data donation apps. Medical data donation refers to the act of voluntarily sharing medical data with research institutions, which plays a crucial role in advancing healthcare research and personalized medicine. To design effective medical data donation apps, we need to understand how privacy expectations affect people's willingness to use such apps. We focus on non-users—those who have no experience with medical data donation apps—because gaining a deeper understanding of their perceptions is essential for fostering the adoption of these apps. Our findings highlight the importance of trust, transparency, and anonymity as driving factors. Participants expressed a willingness to share highly sensitive medical data with the apps if they were assured of complete anonymity, yet criticism regarding the risks of de-anonymization was also raised. Based on our results, we identify privacy awareness issues, especially concerning data sensitivity. Additionally, we explain the differences between participants' privacy expectations and preferences and what existing medical data donation apps offer. Finally, we provide guidance for the development of future user-centric medical data donation apps.

With the increased performance of large language models (LLMs), conversational agents (CA), such as ChatGPT, are nowadays available to any individual requiring little technical knowledge and skills. Initial studies that have investigated related privacy risks primarily focused on either technical aspects and misuse of these tools, or captured overall perceptions of CA users in small-scale qualitative evaluations. Complementing and extending previous work, we used a quantitative user-centered approach to analyze and compare the behaviors and concerns of users and non-users. We conducted a survey study (N=422) with (1) service users, i.e., users of CA services, (2) local users, i.e., users of a local instance of CA (partially local users, or fully local users), and (3) non-users. We collected self-reported usage patterns and personal data-sharing behavior as well as privacy concerns related to different types of personal data (e.g., health data, demographics, or opinions). Furthermore, we analyze individuals' intention to use CA services in multiple scenarios. Our findings show that users of CA services generally have fewer privacy concerns than non-users. While users rarely share data related to personal identifiers and account credentials, they tend to often share data related to lifestyle, health, standard of living, and opinions. Surprisingly, partially local users tend to share more data with CA services as they also generally use CA services more often and for more diverse purposes. Also, while the majority of CA services users declared not being willing to prioritize CA services as an information source in the described scenarios such as seeking legal advice, between about one-quarter and one-third of partially local users would use CA services for all scenarios. Furthermore, half of the users were willing to stop using CA for privacy reasons (e.g., in case of data leaks), whereas a large majority of non-users reported not using CAs simply because they do not have the need or the opportunity. Our work highlights the high privacy risks for CA services users as CA services largely expand the amount of any type of personal information that can be collected by companies.

This paper investigates how experiencing shoulder surfing impacts smartphone users: Through an in-depth survey in the UK (N=91), we specifically investigate how shoulder surfing affects (a) the privacy perceptions of victim users and (b) their interactions with smartphones. We found that the impact of being shoulder-surfed is highly individual. First, shoulder surfing is perceived as unavoidable and frequently occurring, leading to an increased time to complete tasks. Second, users are concerned about their own and other people's privacy and even consider shoulder surfing a gateway to more serious threats (e.g., identity or device theft). Users are willing to alter their behaviour and use software-based protective measures to prevent shoulder surfing. Finally, we captured a set of user-defined criteria essential to software-based protective measures. Based on our results, we discuss future work directions for user-centred shoulder surfing mitigation.

Public administrations provide critical services and manage sensitive data for a country’s citizens. Recent phishing campaigns targeting public sector employees highlight their attractiveness as targets. Deploying state-of-the-art authentication technologies, such as FIDO2, can improve overall security. We conducted a mixed-methods study in Germany to understand better the practices and challenges of deploying passwordless authentication in the public sector. First, we conducted an online survey (N=108) among German public sector employees to gain insights into their experiences and challenges. Next, we partnered with an e-government vendor and performed an in-situ experiment. We let 11 employees from the public sector experience FIDO2 under real-world conditions. Our results show that only a minority of our participants were aware of current passwordless authentication procedures. In our experiment, FIDO2-based methods left an overall positive impression. Hierarchical and heterogeneous public sector structures and the need for more technical expertise and equipment were barriers to adoption.

Collecting user data is ubiquitous in the modern web, because it enables different kinds of services like personalised recommendations. Specific data shared by the users can be analysed further to infer additional information - e.g., gender, age, appearance, or preferences. Such inferences are challenging to predict for users when deciding on whether or not to share data. In this paper, we explore privacy avatars as a way to visualise collected and inferred data to users by a picture. For this, we conducted semi-structured interviews (N=20) where participants were shown privacy avatars based on products they bought in a fictitious online-shopping scenario and additional privacy avatars with a varied level of detail and information. Our results show that participants were shocked by avatars that accurately depicted parts of their visual appearance and perceived inaccurate avatars as privacy-preserving. However, participants preferred information they considered sensitive (e.g., gender) to be accurate creating a tension between privacy and accuracy of shared or inferred information. We conclude with a discussion on how such privacy avatars could be generated and used in practice to offer intuitive and informative privacy interfaces.

PurposePrivacy scales are scientific instruments to measure user attitudes, perspectives, preferences and concerns. However, their ability to reliably capture underlying constructs depends on the individual's interpretation of the scales' statements. For this purpose, this study aims to explore users' understanding of statements of commonly used privacy scales with a representative sample in the UK and compare the results with a similar study conducted with a representative sample from the USA.Design/methodology/approachIn this paper, the authors reproduced the work by Colnago et al. by targeting a representative from the UK (n = 390) instead of the US. They distributed an online survey and asked about the participants' understanding of commonly used privacy constructs such as behaviour, perception, and the like. Participants had to indicate the extent to which they associated each scale statement with each construct.FindingsThis study confirmed that no statement uniquely measured only one construct, and there is a misalignment of scale constructs and users' understanding between the USA and the UK. These findings highlight the need to revisit construct definitions and conduct cross-cultural scale validation studies. This study discusses the possible underlying reasons and best practices for future scale development studies.Research limitations/implicationsLike all online surveys, the results might suffer from response biases, such as wrong self-assessments. Furthermore, since online questionnaires do not involve interaction with an examiner, participants might have misinterpreted the sentiment behind a question. However, this study and the original study by Colnago et al. aimed to investigate precisely that.Practical implicationsThe findings highlight the need to revisit construct definitions and conduct cross-cultural scale validation studies. This study discusses the possible underlying reasons and best practices for future scale development studies. The authors publicly make the data collection and analysis code available to allow for replication and further investigations.Originality/valueThis paper makes the following contributions: (1) Investigation of understanding of scale statements: The authors replicate and extend the findings about the users' understanding of four common privacy scales from Colnago et al. with a representative sample from the UK (n = 390). They provide evidence of a significant misalignment between what each construct is expected to measure and what it is observed to be measuring. While the authors partly confirm the results from Colnago et al., they also provide new insights closer to the UK sample and compare both populations. (2) Recommendations for scale development and validation studies: The results show significant differences between the USA and UK samples even though they share one native language. Based on the original study and related work, the authors present recommendations and lessons learned for scale development and validation. (3) Replication Package: The authors make the data collected and analysis code publicly available to allow for replication and further investigations of further communities, such as speakers of other languages or non-(Western, educated, industrialised, rich and democratic) communities.

Virtual Reality (VR) and Augmented Reality (AR) technologies have gained prominence in scientific and industrial contexts over the last decade. While extensive evaluations of their potential benefits and challenges exist, public perceptions of the psychological and social issues associated with VR and AR remain less explored. This study addresses this gap by examining public attitudes toward these issues and the broader impact of such technologies through a two-part online survey (N=150). Our findings reveal that participants hold a nuanced perspective, clearly distinguishing between VR and AR, expressing optimism about potential applications yet voicing concerns regarding their effects on health, social interactions, and overreliance. This study contributes empirical evidence on public perceptions of VR and AR, offering valuable insights for developers, designers, and policymakers.

Head-mounted displays (HMDs) are increasingly integrated into users' daily lives to provide immersive extended reality (XR) interactions. However, authentication on HMDs can disrupt this immersion because unsuitable 2D methods (e.g., passwords or PINs) are used, or HMDs are not secured at all. This paper presents indepth results of seven co-creation workshops with 24 security and HCI experts to develop novel authentication concepts specifically tailored for HMDs. First, we collected 123 authentication concept ideas. Second, we extracted critical properties to propose overall design requirements for secure and usable interactions (e.g., user awareness, discreetness, and re-purposing of body parts), and security (e.g., resilience to virtual observation) in HMD authentication. We conclude the paper by discussing how schemes can be tailored to the users' circumstances and options to ease the tension between security, usability, and privacy in HMD authentication.

Smart home applications aim to increase convenience, yet often require authentication to protect sensitive data. This is non-trivial: effortful authentication contradicts intended convenience, the multitude of devices raises scalability issues, many devices lack suitable interfaces, and the presence of other inhabitants requires intentional and acceptable interactions. To address these issues, we explored new and creative authentication interactions with an interaction relabelling approach using everyday objects. We conducted six focus group workshops with 20 participants in a living room and a kitchen setting that resulted in a variety of creative authentication interactions with analogue and digital objects. Furthermore, participants created authentication interactions based on tasks that they have to or wish to perform anyway such as cleaning the kitchen - thus primary tasks. This led us to explore the option to transform authentication from being an additional, secondary task towards using primary tasks further in an online study with 194 participants. Relevant implications in terms of acceptable authentication task characteristics, user perceptions, arising security challenges, and psychological habit research are discussed.

Physical interactions with socially assistive robots (SARs) positively affect user wellbeing. However, haptic experiences when touching a SAR are typically limited to perceiving the robot's movements or shell texture, while other modalities that could enhance the touch experience with the robot, such as vibrotactile stimulation, are under-explored. In this exploratory qualitative study, we investigate the potential of enhancing human interaction with the PARO robot through vibrotactile heartbeats, with the goal to regulate subjective wellbeing during stressful situations. We conducted in-depth one-on-one interviews with 30 participants, who watched three horror movie clips alone, with PARO, and with a PARO that displayed a vibrotactile heartbeat. Our findings show that PARO's presence and its interactive capabilities can help users regulate emotions through attentional redeployment from a stressor toward the robot. The vibrotactile heartbeat further reinforced PARO's physical and social presence, enhancing the socio-emotional support provided by the robot and its perceived life-likeness. We discuss the impact of individual differences in user experience and implications for the future design of life-like vibrotactile stimulation for SARs.

Mobile phones are most likely the subject of targeted attacks, such as software exploits. The resources needed to carry out such attacks are becoming increasingly available and, hence, easily executable, putting users' privacy at risk. We conducted a systematic literature analysis to understand the relationship between resources and attack feasibility and present a categorisation of social engineering and side-channel attacks on mobile phones focusing on the resources attackers require. Our proposed categorisation levels facilitate an in-depth understanding of how mobile phone attacks can be executed using different combinations of partly simple resources. The analysis reveals that discrete protection mechanisms are insufficient to provide all-inclusive protection. The proposed categorisation assists in building novel solutions for safeguarding users' privacy from diverse attacks by carefully considering the potential misuse of resources. We conclude by outlining future research directions highlighting the urgent need for a holistic user defense.

This paper proposes an Out-of-Device Privacy Scale (ODPS) - a reliable, validated psychometric privacy scale that measures users’ importance of out-of-device privacy. In contrast to existing scales, ODPS is designed to capture the importance individuals attribute to protecting personal information from out-of-device threats in the physical world, which is essential when designing privacy protection mechanisms. We iteratively developed and refined ODPS in three high-level steps: item development, scale development, and scale validation, with a total of N=1378 participants. Our methodology included ensuring content validity by following various approaches to generate items. We collected insights from experts and target audiences to understand response variability. Next, we explored the underlying factor structure using multiple methods and performed dimensionality, reliability, and validity tests to finalise the scale. We discuss how ODPS can support future work predicting user behaviours and designing protection methods to mitigate privacy risks.

This paper explores how personal attributes, such as age, gender, technological expertise, or “need for touch”, correlate with people’s preferences for properties of tangible privacy protection mechanisms, for example, physically covering a camera. For this, we conducted an online survey (N = 444) where we captured participants’ preferences of eight established tangible privacy mechanisms well-known in daily life, their perceptions of effective privacy protection, and personal attributes. We found that the attributes that correlated most strongly with participants’ perceptions of the established tangible privacy mechanisms were their “need for touch” and previous experiences with the mechanisms. We use our findings to identify desirable characteristics of tangible mechanisms to better inform future tangible, digital, and mixed privacy protections. We also show which individuals benefit most from tangibles, ultimately motivating a more individual and effective approach to privacy protection in the future.

Medical data donation involves voluntarily sharing medical data with research institutions, which is crucial for advancing healthcare research. However, the sensitive nature of medical data poses privacy and security challenges. The primary concern is the risk of de-anonymization, where users can be linked to their donated data through background knowledge or communication metadata. In this paper, we introduce Anonify, a decentralized anonymity protocol offering strong user protection during data donation without reliance on a single entity. It achieves dual-level anonymity protection, covering both communication and data aspects by leveraging Distributed Point Functions, and incorporating k-anonymity and stratified sampling within a secret-sharing-based setting. Anonify ensures that the donated data is in a form that affords flexibility for researchers in their analyses. Our evaluation demonstrates the efficiency of Anonify in preserving privacy and optimizing data utility. Furthermore, the performance of machine learning algorithms on the anonymized datasets generated by the protocol shows high accuracy and precision.

Personalized privacy assistants (PPAs) communicate privacy-related decisions of their users to Internet of Things (IoT) devices. There are different ways to implement PPAs by varying the degree of autonomy or decision model. This paper investigates user perceptions of PPA autonomy models and privacy profiles – archetypes of individual privacy needs – as a basis for PPA decisions in private environments (e.g., a friend’s home). We first explore how privacy profiles can be assigned to users and propose an assignment method. Next, we investigate user perceptions in 18 usage scenarios with varying contexts, data types and number of decisions in a study with 1126 participants. We found considerable differences between the profiles in settings with few decisions. If the number of decisions gets high (> 1/h), participants exclusively preferred fully autonomous PPAs. Finally, we discuss implications and recommendations for designing scalable PPAs that serve as privacy interfaces for future IoT devices.

Scale questionnaires are psychometric tools that capture perspectives and experiences. Consequently, these tools need to be reliable and valid. In this paper, we investigate the impact of response widgets - the UI elements that allow users to answer scale items - on the overall scale reliability and construct validity of three varied length scale questionnaires in a user study (N=30). Our results reveal that optimum reliability was achieved using radio buttons and dropdowns in all varied-length questionnaires. Further, valid results were produced utilising the slider and dropdown. No significant differences were found in time consumption, but click count was significantly higher with dropdown. Radio buttons scored lower in format satisfaction than others, and dropdown was the least effective in ease of selection and quick completion. In light of these results, we conclude that response widgets are more than just aesthetics and should be selected as per the researcher’s aims.