Head-worn augmented reality (AR) continues to evolve through critical advancements in power optimizations, AI capabilities, and naturalistic user interactions. Eye-tracking sensors play a key role in these advancements. At the same time, eye-tracking data is not well understood by users and can reveal sensitive information. Our work contributes visualizations based on visceral notice to increase privacy awareness of eye-tracking data in AR. We also evaluated user perceptions towards privacy noise mechanisms applied to gaze data visualized through these visceral interfaces. While privacy mechanisms have been evaluated against privacy attacks, we are the first to evaluate them subjectively and understand their influence on data-sharing attitudes. Despite our participants being highly concerned with eye-tracking privacy risks, we found 47% of our participants still felt comfortable sharing raw data. When applying privacy noise, 70% to 76% felt comfortable sharing their gaze data for the Weighted Smoothing and Gaussian Noise privacy mechanisms, respectively. This implies that participants are still willing to share raw gaze data even though overall data-sharing sentiments decreased after experiencing the visceral interfaces and privacy mechanisms. Our work implies that increased access and understanding of privacy mechanisms are critical for gaze-based AR applications; further research is needed to develop visualizations and experiences that relay additional information about how raw gaze data can be used for sensitive inferences, such as age, gender, and ethnicity. We intend to open-source our codebase to provide AR developers and platforms with the ability to better inform users about privacy concerns and provide access to privacy mechanisms. A pre-print of this paper and all supplemental materials are available at https://bmdj-vt.github.io/project_pages/privacy_notice.

Eye tracking is increasingly being integrated into virtual reality (VR) devices to support a wide range of applications. It is used as a method of interaction, to support performance optimizations, and to create adaptive trainingor narrative experiences. However, providing access to eye-tracking data also introduces the ability to monitor user activity, detect and classify a user’s biometric identity, or otherwise reveal sensitive information such as medical conditions. As this technology continues to evolve, users should be made aware of the amount of information they are sharing about themselves to developers and how it can be used. While traditional terms of service may relay this type of information, previous work indicates they are not accessibly conveying privacy-related information to users. Considering this problem, we suggest the application of visceral interfaces that are designed to inform users about eye-tracking data within the VR experience. To this end, we designed and conducted a user study on three visceral interfaces to educate users about their eye-tracking data. Our results suggest that while certain visualizations can be distracting, participants ultimately found them informative and supported the development and availability of such interfaces even if they are not enabled by default or always enabled. Our research contributes to developing informative interfaces specific to eye tracking that promote transparency and privacy awareness in data collection for VR.

Is simulation some new kind of science? We argue that instead simulation fits smoothly into existing scientific practice, but does so in several importantly different ways. Simulations in general, and computer simulations in particular, ought to be understood as techniques which, like many scientific techniques, can be employed in the service of various and diverse epistemic goals. We focus our attentions on the way in which simulations can function as (i) explanatory and (ii) predictive tools. We argue that a wide variety of simulations, both computational and physical, are best conceived in terms of a set of common features: initial or input conditions, a mechanism or set of rules, and a set of results or output conditions. Studying simulations in these terms yields a new understanding of their character as well as a body of normative recommendations for the care and feeding of scientific simulations.