Kevin worked on crea/ng the VR applica/on, popula/ng it with tracked objects, and implemen/ng the cat minigame. Yuxin worked on implemen/ng both forms of tracking (gaze and reac/on /me), integra/ng them with the VR applica/on, and expor/ng data. Wentao worked on overall integra/on (including adding more tracked objects and debugging the VR applica/on), the R script for data analysis, and designing/running the pilot user studies. 1 Introduc9on Virtual reality (VR) applica/ons expose users to poten/ally unfamiliar forms of data collec/on, including gaze tracking and a wide range of mo/on and posi/on sensors. As VR becomes more popular, this data will likely be used by companies to make inferences about users, as is common nowadays across the Internet; however, given the rela/ve novelty of these new forms of tracking, VR users may not know of or understand the changes and implica/ons involved. We designed and piloted a user study designed to answer two research ques/ons: 1. What are people’s percep/ons of tracking and inference in VR when reﬂec/ng on examples drawn from their own ac/vity? How does perceived acceptability diﬀer by context? 2. How does awareness of possible tracking and inference aﬀect people’s aRtudes towards VR, such as their expressed level of comfort par/cipa/ng in VR? Copyright is held by the author/owner. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee. USENIX Symposium on Usable Privacy and Security (SOUPS) 2023. August 6–8, 2023, Anaheim, CA, USA In the rest of this report, we brieﬂy discuss related work; explain our study method, including the design of the VR environment we created to expose par/cipants to tracking; highlight some interes/ng ﬁndings from two pilot studies; and discuss how we plan to reﬁne our method in prepara/on for a full user study. 2 Background We ﬁrst present background informa/on on tracking and inference in VR, before reviewing related studies on user percep/ons of tracking and inference both in VR and in other contexts. 2.1 Tracking and inference in VR Previous ar/cles and papers have extensively discussed tracking and inference in VR. O’Brolcháin et al. categorized threats posed by VR social networks to privacy and autonomy [12]. Kelly et al. evaluated seven popular VR devices (covering all major VR companies) and ﬁve popular third-party VR apps, analyzing what data companies collect and what they intend to do with personal informa/on [6]. They found that all appear to track and mone/ze data, whether by selling it to third par/es or by using it to drive marke/ng and target ads. Others have raised alarms about con/nuing threats to privacy [4,14], focused on speciﬁc forms of tracking as par/cularly new and concerning, including tracking eyes [2], facial expressions [5], and digital bodies (realis/c avatars) [7]. In addi/on to expanding the forms of tracking, VR is also immersive in a way that is dis/nc/ve from most other applica/ons. While a ﬁtness tracker may collect a user’s physiological responses, such as heart rate, a VR applica/on can know exactly what is in the user’s environment that might prompt certain responses, and it can even alter the environment to test those responses. This raises addi/onal concerns for user privacy in VR. Some researchers have implemented and tested proofs of concept for novel forms of tracking and inference in VR, such as iden/fying users from movement [8,13]. S/ll others have developed privacy-enhancing tools to protect users from tracking [9,15]. In a user study similar to our own, Nair et al. designed a 2 VR escape room designed to secretly harvest personal data from users in order to make inferences; they found that they could make accurate inferences without /pping oﬀ users, who were surprised by the breadth of data collec/on [11]. While their study shows that bespoke VR environments can be designed to make accurate inferences about users, ours aims to add passive tracking in more natural VR contexts, and our research goals are primarily concerned with users’ reac/ons rather than accuracy. 2.2 User percep9ons of tracking Limited prior work on user percep/ons suggests that novel forms of tracking and inference in VR may not be top of mind for users. VR users interviewed by Adams et al. in 2018 were primarily concerned about data collec/on from microphones and infrared sensors, and some felt that privacy would become a greater issue in the future [1]. In 2015, MoR et al. analyzed comments posted online and found minimal privacy concern around VR headsets [10]. Through our study, we aim to make privacy issues more tangible for users in the present so as to explore their reac/ons and percep/ons. Other studies outside of the VR context have taken a similar approach of showing par/cipants how they were tracked in order to elicit reac/ons. Weinshel et al. created a browser extension that mimicked third-party trackers and visualized tracking data for users [18]; in a longitudinal ﬁeld study, they found that this tool helped par/cipants understand tracking beher and led to increased intent to take privacy-preserving ac/ons. Wei et al. showed par/cipants their Twiher ad targe/ng data, iden/fying prac/ces that felt more invasive and sugges/ng improvements to ad targe/ng explana/ons [17]. We take a similar approach, simula/ng tracking in VR for our par/cipants and asking ques/ons to understand their percep/ons. 3 Method In this sec/on, we ﬁrst describe how we designed a VR applica/on to collect data from par/cipants. We then describe our study method, in which par/cipants interacted in this VR environment and then answered ques/ons about their experience and percep/ons. We note that the primary purpose of the VR ac/vity was not to make accurate inferences about par/cipants, but rather to simulate a company’s process for making inferences plausibly enough that the experience would help par/cipants explore new perspec/ves. 3.1 Tracking par9cipants in a VR applica9on As the backbone of our user study, we created a VR applica/on in Unity, star/ng from a free apartment asset in the Unity Asset Store. We chose an apartment because visi/ng a friend’s virtual space is a common ac/vity in VR social networks. To track user p