In social virtual reality (social VR), users are threatened by potential cybercrimes, such as identity theft, sensitive data breaches, and embodied harassment. These concerns are heightened by the increasing interest in the metaverse, the advancements in photorealistic 3D user reconstructions, and the rising incidents of online privacy violations. Designing secure social VR applications that protect users while enhancing their experience, acceptance and trust remains a challenge. This article investigates potential identity management solutions in social VR, and their impacts on usability and user acceptance. We developed a social VR prototype with novel and established countermeasures, including motion biometric verification, and conducted a study with 52 participants. Our findings reveal diverse preferences for identity management and underscore the importance of authenticity, autonomy, and reciprocity. Key findings include: passive verification is favored for pragmatic user experience, while active verification is preferred for its hedonic quality; continuous or periodic verification strengthens users’ confidence in their privacy; and while user awareness promotes authentic engagement, it may also diminish the willingness to disclose personal information. This research not only offers foundational insights into the evaluated scenarios and countermeasures, but also sheds light on the designs of more trustworthy and inclusive social VR applications.

This paper introduces “Motion Passwords”, a novel biometric authentication approach where virtual reality users verify their identity by physically writing a chosen word in the air with their hand controller. This method allows combining three layers of verification: knowledge-based password input, handwriting style analysis, and motion profile recognition. As a first step towards realizing this potential, we focus on verifying users based on their motion profiles. We conducted a data collection study with 48 participants, who performed over 3800 Motion Password signatures across two sessions. We assessed the effectiveness of feature-distance and similarity-learning methods for motion-based verification using the Motion Passwords as well as specific and uniform ball-throwing signatures used in previous works. In our results, the similarity-learning model was able to verify users with the same accuracy for both signature types. This demonstrates that Motion Passwords, even when applying only the motion-based verification layer, achieve reliability comparable to previous methods. This highlights the potential for Motion Passwords to become even more reliable with the addition of knowledge-based and handwriting style verification layers. Furthermore, we present a proof-of-concept Unity application demonstrating the registration and verification process with our pretrained similarity-learning model. We publish our code, the Motion Password dataset, the pretrained model, and our Unity prototype on https://github.com/cschell/MoPs

This demo presents an innovative and validated method for measuring interpersonal trust in virtual humans as social interaction partners. In our design, participants are tasked to navigate through a virtual reality maze and interact with a virtual human, seeking advice on which path to take. By carefully designing the experiments and context, we induced participants to believe that they are interacting with a real person. A validation study, manipulating virtual humans' appearance, tone of voice, and engagement, revealed that the frequency of advice-seeking in the maze correlates with participants' evaluation of interpersonal trust towards virtual humans. Thus, our paradigm represents a valuable research tool for studying trust in virtual reality.

Virtual humans, including virtual agents and avatars, play an increasingly important role as VR technology advances. For example, virtual humans are used as digital bodies of users in social VR or as interfaces for AI assistants in online financing. Interpersonal trust is an essential prerequisite in real-life interactions, as well as in the virtual world. However, to date, there are no established interpersonal trust measurement tools specifically for virtual humans in virtual reality. This study fills the gap, by contributing a novel validated behavioural tool to measure interpersonal trust towards a specific virtual social interaction partner in social VR. This validated paradigm is inspired by a previously proposed virtual maze task that measures trust towards virtual characters. In the current study, a variant of this paradigm was implemented. The task of the users (the trustors) is to navigate through a maze in virtual reality, where they can interact with a virtual human (the trustee). They can choose to 1) ask for advice and 2) follow the advice from the virtual human if they want to. These measures served as behavioural measures of trust. We conducted a validation study with 70 participants in a between-subject design. The two conditions did not differ in the content of the advice but in the appearance, tone of voice and engagement of the trustees (alleged as avatars controlled by other participants). Results indicate that the experimental manipulation was successful, as participants rated the virtual human as more trustworthy in the trustworthy condition than in the untrustworthy condition. Importantly, this manipulation affected the trust behaviour of our participants, who, in the trustworthy condition, asked for advice more often and followed advice more often, indicating that the paradigm is sensitive to assessing interpersonal trust towards virtual humans. Thus, our paradigm can be used to measure differences in interpersonal trust towards virtual humans and may serve as a valuable research tool to study trust in virtual reality.

Photorealistic avatars show great potential in social VR and VR collaboration. However, identity and privacy issues are threatening avatars' authenticity in social VR. In addition to the necessary authentication and protection, effective solutions are needed to convey avatars' authenticity status to users and thereby enhance the overall trustworthiness. We designed several visual indicators (VIs) using static or dynamic visual effects on photorealistic avatars and evaluated their effectiveness in visualizing avatars' authenticity status. In this study we explored suitable attributes and designs for conveying the authenticity of photorealistic avatars and influencing their perceived trustworthiness. Furthermore, we investigated how different interactivity levels influence their effectiveness (the avatar was either presented in a static image, an animated video clip, or an immersive virtual environment). Our findings showed that using a full name can increase trust, while most other VIs could decrease users' trust. We also found that interactivity levels significantly impacted users' trust and the effectiveness of VIs. Based on our results, we developed design guidelines for visual indicators as effective tools to convey authenticity, as a first step towards the improvement of trustworthiness in social VR with identity management.