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SoK: Data Privacy in Virtual Reality
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The adoption of virtual reality (VR) technologies has rapidly gained momentum in recent years as companies around the world begin to position the so-called "metaverse" as the next major medium for accessing and interacting with the internet. While consumers have become accustomed to a degree of data harvesting on the web, the real-time nature of data sharing in the metaverse indicates that privacy concerns are likely to be even more prevalent in the new "Web 3.0." Research into VR privacy has demonstrated that a plethora of sensitive personal information is observable by various would-be adversaries from just a few minutes of telemetry data. On the other hand, we have yet to see VR parallels for many privacy-preserving tools aimed at mitigating threats on conventional platforms. This paper aims to systematize knowledge on the landscape of VR privacy threats and countermeasures by proposing a comprehensive taxonomy of data attributes, protections, and adversaries based on the study of 68 collected publications. We complement our qualitative discussion with a statistical analysis of the risk associated with various data sources inherent to VR in consideration of the known attacks and defenses. By focusing on highlighting the clear outstanding opportunities, we hope to motivate and guide further research into this increasingly important field.
Forward citations
Cited by 3 Pith papers
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From Perception to Protection: A Developer-Centered Study of Security and Privacy Threats in Extended Reality (XR)
A 23-developer interview study shows professional XR developers recall few XR-specific threats unprompted, rate unfamiliar attacks lower, and exhibit awareness gaps plus diffusion of responsibility.
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SoK: Come Together -- Unifying Security, Information Theory, and Cognition for a Mixed Reality Deception Attack Ontology & Analysis Framework
The authors construct the MR Deception Analysis Framework, an ontology plus two conceptual models for analyzing deception attacks in Mixed Reality.
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VReaves: Eavesdropping on Virtual Reality App Identity and Activity via Electromagnetic Side Channels
Electromagnetic emanations from a VR headset can be classified with a nearby software-defined radio and a fine-tuned ResNet to identify the running VR app and the user's activity, with a claimed accuracy near 99%.
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