research-article

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Going Incognito in the Metaverse: Achieving Theoretically Optimal Privacy-Usability Tradeoffs in VR

Authors:

Gonzalo Munilla-Garrido,

Dawn SongAuthors Info & Claims

UIST '23: Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology

Article No.: 61, Pages 1 - 16

https://doi.org/10.1145/3586183.3606754

Published: 29 October 2023 Publication History

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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 ε-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.

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Index Terms

Going Incognito in the Metaverse: Achieving Theoretically Optimal Privacy-Usability Tradeoffs in VR
1. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Privacy protections

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cover image ACM Conferences

UIST '23: Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology

October 2023

1825 pages

ISBN:9798400701320

DOI:10.1145/3586183

Editors:
Sean Follmer
Stanford University, USA
,
Jeff Han,
Jürgen Steimle
Saarland University, Germany
,
Nathalie Henry Riche
Microsoft Research, USA

Copyright © 2023 Owner/Author.

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Published: 29 October 2023

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UIST '23

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UIST '23: The 36th Annual ACM Symposium on User Interface Software and Technology

October 29 - November 1, 2023

CA, San Francisco, USA

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Bozkir EÖzdel SLau KWang MGao HKasneci E(2024)Embedding Large Language Models into Extended Reality: Opportunities and Challenges for Inclusion, Engagement, and PrivacyProceedings of the 6th ACM Conference on Conversational User Interfaces10.1145/3640794.3665563(1-7)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3640794.3665563
Liebers JLaskowski PRademaker FSabel LHoppen JGruenefeld USchneegass S(2024)Kinetic Signatures: A Systematic Investigation of Movement-Based User Identification in Virtual RealityProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642471(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642471
Nair VGuo WO'Brien JRosenberg LSong D(2024)Deep Motion Masking for Secure, Usable, and Scalable Real-Time Anonymization of Ecological Virtual Reality Motion Data2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)10.1109/VRW62533.2024.00096(493-500)Online publication date: 16-Mar-2024
https://doi.org/10.1109/VRW62533.2024.00096
Nair VRack CGuo WWang RLi SHuang BCull AO'Brien JLatoschik MRosenberg LSong D(2024)Inferring Private Personal Attributes of Virtual Reality Users from Ecologically Valid Head and Hand Motion Data2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)10.1109/VRW62533.2024.00094(477-484)Online publication date: 16-Mar-2024
https://doi.org/10.1109/VRW62533.2024.00094
Nair VGuo WWang RO'Brien JRosenberg LSong D(2024)Berkeley Open Extended Reality Recordings 2023 (BOXRR-23): 4.7 Million Motion Capture Recordings from 105,000 XR UsersIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.337208730:5(2239-2246)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3372087
Warin CSeeger DShams SReinhardt D(2024)PrivXR: A Cross-Platform Privacy-Preserving API and Privacy Panel for Extended Reality2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops59983.2024.10503341(417-420)Online publication date: 11-Mar-2024
https://doi.org/10.1109/PerComWorkshops59983.2024.10503341
Wei YSun YZheng SHsu HHuang CHsu C(2024)Mitigating Privacy Threats Without Degrading Visual Quality of VR Applications: Using Re-Identification Attack as a Case Study2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR62202.2024.00040(214-220)Online publication date: 7-Aug-2024
https://doi.org/10.1109/MIPR62202.2024.00040
Letafati MOtoum S(2024)Digital Healthcare in the Metaverse: Insights into Privacy and SecurityIEEE Consumer Electronics Magazine10.1109/MCE.2023.333449413:3(80-89)Online publication date: May-2024
https://doi.org/10.1109/MCE.2023.3334494
Wei XHellge CYang CSon J(2024)Improving QoE-Privacy Tradeoff in XR StreamingIEEE Signal Processing Letters10.1109/LSP.2024.340161631(1504-1508)Online publication date: 2024
https://doi.org/10.1109/LSP.2024.3401616
Letafati MOtoum S(2023)Global Differential Privacy for Distributed Metaverse Healthcare Systems2023 International Conference on Intelligent Metaverse Technologies & Applications (iMETA)10.1109/iMETA59369.2023.10294469(01-08)Online publication date: 18-Sep-2023
https://doi.org/10.1109/iMETA59369.2023.10294469
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