Going Incognito in the Metaverse: Achieving Theoretically Optimal Privacy-Usability Tradeoffs in VR (2208.05604v5)

Abstract: Virtual reality (VR) telepresence applications and the so-called "metaverse" promise to be the next major medium of human-computer interaction. However, with recent studies demonstrating the ease at which VR users can be profiled and deanonymized, metaverse platforms carry many of the privacy risks of the conventional internet (and more) while at present offering few of the defensive utilities that users are accustomed to having access to. To remedy this, we present the first known method of implementing an "incognito mode" for VR. Our technique leverages local differential privacy to quantifiably obscure sensitive user data attributes, with a focus on intelligently adding noise when and where it is needed most to maximize privacy while minimizing usability impact. Our system is capable of flexibly adapting to the unique needs of each VR application to further optimize this trade-off. We implement our solution as a universal Unity (C#) plugin that we then evaluate using several popular VR applications. Upon faithfully replicating the most well-known VR privacy attack studies, we show a significant degradation of attacker capabilities when using our solution.

• The paper introduces a VR incognito mode that uses local ε-differential privacy to obscure sensitive telemetry data.
• It applies a bounded Laplace mechanism and randomized response to provide quantifiable protection, reducing deanonymization risk by up to 96%.
• The approach is implemented as a universal Unity plugin, allowing users to fine-tune privacy settings with minimal impact on usability.

Achieving Optimal Privacy-Usability Tradeoffs in VR: A Critical Summary of "Going Incognito in the Metaverse"

This paper, presented at the 2023 ACM Symposium on User Interface Software and Technology, addresses a crucial challenge in virtual reality (VR): achieving a balance between user privacy and utility. As VR technology advances, the use of telepresence applications in the "metaverse" increasingly exposes users to privacy risks, such as profiling and deanonymization based on their telemetry data. Despite these concerns, current VR frameworks lack the protective measures that are commonplace in traditional web browsers, such as "incognito mode." This paper proposes a novel solution to this disparity through the application of differential privacy techniques.

Methodological Overview

The authors propose the first known method for implementing an "incognito mode" in VR environments by leveraging local $\varepsilon$-differential privacy. The approach aims to obscure sensitive user data by intelligently adding noise at critical points to minimize usability impacts. This system adapts flexibly to the privacy and usability needs of various VR applications, and is implemented as a universal Unity plugin compatible with multiple popular VR platforms.

The work is grounded on the concept that user privacy can be protected effectively by adding statistically calibrated noise to telemetry data streams, thus hindering the ability of adversaries to identify or infer sensitive data attributes. The solution relies on key VR attributes such as height, wingspan, and interpupillary distance being obfuscated via differential privacy. By using a "bounded Laplace mechanism" for continuous attributes and randomized response for boolean data, the authors demonstrate quantifiable privacy guarantees for user data.

Key Results and Evaluation

The authors evaluate their solution against previously published VR privacy attacks, showing that their approach significantly degrades the capability of attackers. The evaluations, backed by empirical data from earlier studies involving thousands of sessions, indicate a marked reduction in privacy risk. For instance, the capability of adversaries to deanonymize users was reduced by as much as 96% with the system in place. The tradeoffs were evaluated through a set of $\varepsilon$-values which allowed users to toggle between low, medium, and high privacy settings.

Furthermore, the proposed system addresses numerous attributes that adversaries target in VR. These include , but are not limited to, physical metrics like height and wingspan, demographic attributes such as gender and age, and network-based attributes like latency that could leak geolocation information. By merely implementing a client-side plugin, the proposed solutions cover a wide spectrum of possible attackers, from the server-side to end-user eavesdroppers.

Implications and Future Directions

The implications of this work are multifaceted. From a theoretical perspective, the paper makes a compelling case for the adaptation of well-established differential privacy techniques to the emerging domain of VR, providing a measurable privacy framework that balances fidelity and protection. In practical terms, if adopted widely, a VR-based incognito mode could offer substantial empowerment to users who wish to protect their identity and data when navigating the metaverse.

Moving forward, this paper sets the stage for further exploration into firmware-level implementations that could bolster the defense against hardware-centric attacks, which are beyond the current scope. Additionally, more research is warranted to address other emerging VR threats, such as those involving full-body and eye-tracking data, which were identified but not extensively covered in this paper.

Conclusion

Overall, this paper presents a comprehensive solution to an increasingly pressing issue in the virtual reality landscape. By borrowing concepts from web privacy and adapting them to the unique demands of VR, the implementation of an "incognito mode" stands as a promising evolution towards safer, more private user experiences in the metaverse. As the metaverse continues to develop, innovations like these will be invaluable in safeguarding user interactions and building trust in virtual environments.