Gender Identification of VR Users by Machine Learning Tracking Data DOI

Qidi J. Wang,

Ryan P. McMahan

2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Journal Year: 2024, Volume and Issue: unknown, P. 827 - 828

Published: March 16, 2024

Gender identification of virtual reality (VR) users by machine learning tracking data could afford personalized experiences, including mitigation expected human factors or psychology issues. While much research has recently been conducted to identify individual given their VR data, little investigated gender identification. Furthermore, nearly all prior studies have only considered positions and rotations the devices. We present a systematic investigation different combinations spatial representations tracked devices for predicting user's gender. Our results indicate head are integral while surprisingly not as important.

Language: Английский

5G/6G-enabled metaverse technologies: Taxonomy, applications, and open security challenges with future research directions DOI

Muhammad Adil,

Houbing Song, Muhammad Khurram Khan

et al.

Journal of Network and Computer Applications, Journal Year: 2024, Volume and Issue: 223, P. 103828 - 103828

Published: Jan. 19, 2024

Language: Английский

Citations

28

Mediverse Beyond Boundaries: A Comprehensive Analysis of AR and VR Integration in Medical Education for Diverse Abilities DOI Creative Commons
Abdul Khader Jilani Saudagar, Abhishek Kumar, Muhammad Badruddin Khan

et al.

Deleted Journal, Journal Year: 2024, Volume and Issue: 3(1)

Published: Jan. 30, 2024

This research paper explores the pioneering role of augmented reality (AR) and virtual (VR) in reshaping medical education within metaverse, focusing particularly on their remarkable benefits for individuals with disabilities. examines how these immersive technologies can be customized to meet unique needs those disabilities, including mobility. It demonstrates AR VR enable actively participate simulations, offering them a deeper understanding intricate procedures. article highlights critical importance ethical considerations, privacy measures, adherence accessibility standards deployment training robust framework harnessing transformative capabilities health education. delves into various ways which facilitate experiential learning, providing an immersive, hands-on approach supporting remote diagnostics mental services, showcasing capability enhance doctor–patient interactions support. represents that metaverse have potential empower leading more inclusive effective training.

Language: Английский

Citations

11

Privacy preservation in Artificial Intelligence and Extended Reality (AI-XR) metaverses: A survey DOI Creative Commons
Mahdi Alkaeed, Adnan Qayyum, Junaid Qadir

et al.

Journal of Network and Computer Applications, Journal Year: 2024, Volume and Issue: 231, P. 103989 - 103989

Published: Aug. 2, 2024

The metaverse is a nascent concept that envisions virtual universe, collaborative space where individuals can interact, create, and participate in wide range of activities. Privacy the critical concern as evolves immersive experiences become more prevalent. privacy problem refers to challenges concerns surrounding personal information data within Virtual Reality (VR) environments shared VR becomes accessible. Metaverse will harness advancements from various technologies such Artificial Intelligence (AI), Extended (XR) Mixed (MR) provide personalized services its users. Moreover, enable experiences, relies on collection fine-grained user leads issues. Therefore, before potential be fully realized, related must addressed. This includes safeguarding users' control over their data, ensuring security information, protecting in-world actions interactions unauthorized sharing. In this paper, we explore future metaverses are expected face, given reliance AI for tracking users, creating XR MR facilitating interactions. thoroughly analyze technical solutions differential privacy, Homomorphic Encryption, Federated Learning discuss sociotechnical issues regarding privacy.

Language: Английский

Citations

11

Exploring the Privacy Risks of Adversarial VR Game Design DOI Creative Commons
Vivek Nair, Gonzalo Munilla Garrido, Dawn Song

et al.

Proceedings on Privacy Enhancing Technologies, Journal Year: 2023, Volume and Issue: 2023(4), P. 238 - 256

Published: Aug. 3, 2023

Fifty study participants playtested an innocent-looking "escape room" game in virtual reality (VR). Within just a few minutes, adversarial program had accurately inferred over 25 of their personal data attributes, from anthropometrics like height and wingspan to demographics age gender. As notoriously data-hungry companies become increasingly involved VR development, this experimental scenario may soon represent typical user experience. Since the Cambridge Analytica scandal 2018, adversarially-designed gamified elements have been known constitute significant privacy threat conventional social platforms. In work, we present case how metaverse environments can similarly be adversarially constructed covertly infer dozens attributes seemingly-anonymous users. While existing research largely focuses on passive observation, argue that because individuals subconsciously reveal information via motion response specific stimuli, active attacks pose outsized risk environments.

Language: Английский

Citations

19

SoK: Data Privacy in Virtual Reality DOI Creative Commons
Gonzalo Munilla Garrido, Vivek Nair, Dawn Song

et al.

Proceedings on Privacy Enhancing Technologies, Journal Year: 2023, Volume and Issue: 2024(1), P. 21 - 40

Published: Oct. 22, 2023

The adoption of virtual reality (VR) technologies has rapidly gained momentum in recent years as companies around the world begin to position so-called ''metaverse'' next major medium for accessing and interacting with internet. While consumers have become accustomed a degree data harvesting on web, real-time nature sharing metaverse indicates that privacy concerns are likely be even more prevalent new ''Web 3.0.'' Research into VR demonstrated plethora sensitive personal information is observable by various would-be adversaries from just few minutes telemetry data. On other hand, we yet see parallels many privacy-preserving tools aimed at mitigating threats conventional platforms. This paper aims systematize knowledge landscape countermeasures proposing comprehensive taxonomy attributes, protections, based study 74 collected publications. We complement our qualitative discussion statistical analysis risk associated sources inherent consideration known attacks defenses. By focusing highlighting clear outstanding opportunities, hope motivate guide further research this increasingly important field.

Language: Английский

Citations

14

Berkeley Open Extended Reality Recordings 2023 (BOXRR-23): 4.7 Million Motion Capture Recordings from 105,000 XR Users DOI
Vivek Nair, Wenbo Guo, Rui Wang

et al.

IEEE Transactions on Visualization and Computer Graphics, Journal Year: 2024, Volume and Issue: 30(5), P. 2239 - 2246

Published: March 4, 2024

Extended reality (XR) devices such as the Meta Quest and Apple Vision Pro have seen a recent surge in attention, with motion tracking "telemetry" data lying at core of nearly all XR metaverse experiences. Researchers are just beginning to understand implications this for security, privacy, usability, more, but currently lack large-scale human datasets study. The BOXRR-23 dataset contains 4,717,215 capture recordings, voluntarily submitted by 105,852 device users from over 50 countries. is 200 times larger than largest existing research uses new, highly efficient purpose-built Open Recording (XROR) file format.

Language: Английский

Citations

4

Deep Motion Masking for Secure, Usable, and Scalable Real-Time Anonymization of Ecological Virtual Reality Motion Data DOI
Vivek Nair, Wenbo Guo, James F. O’Brien

et al.

2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Journal Year: 2024, Volume and Issue: unknown, P. 493 - 500

Published: March 16, 2024

Virtual reality (VR) and "metaverse" systems have recently seen a resurgence in interest investment as major technology companies continue to enter the space. However, recent studies demonstrated that motion tracking "telemetry" data used by nearly all VR applications is uniquely identifiable fingerprint scan, raising significant privacy concerns surrounding metaverse technologies. In this paper, we propose new "deep masking" approach scalably facilitates real-time anonymization of telemetry data. Through large-scale user study $(N=182)$ , demonstrate our method significantly more usable private than existing anonymity systems.

Language: Английский

Citations

4

Navigating Privacy Challenges in the Metaverse: A Comprehensive Examination of Current Technologies and Platforms DOI
Lamiaa Basyoni,

Aliya Tabassum,

Khaled Shaban

et al.

IEEE Internet of Things Magazine, Journal Year: 2024, Volume and Issue: 7(4), P. 144 - 152

Published: June 27, 2024

Language: Английский

Citations

4

Exploring the Uncoordinated Privacy Protections of Eye Tracking and VR Motion Data for Unauthorized User Identification DOI
Samantha Aziz, Oleg V. Komogortsev

Published: March 8, 2025

Language: Английский

Citations

0

Kinetic Signatures: A Systematic Investigation of Movement-Based User Identification in Virtual Reality DOI Creative Commons
Jonathan Liebers, Patrick Laskowski, Florian Rademaker

et al.

Published: May 11, 2024

Behavioral Biometrics in Virtual Reality (VR) enable implicit user identification by leveraging the motion data of users' heads and hands from their interactions VR. This spatiotemporal forms a Kinetic Signature, which is user-dependent behavioral biometric trait. Although kinetic signatures have been widely used recent research, factors contributing to degree identifiability remain mostly unexplored. Drawing existing literature, this work systematically examines influence static dynamic components human motion. We conducted study (N = 24) with two sessions reidentify users across different VR sports exercises after one week. found that signature depends on its inherent factors, best combination allowing for 90.91% accuracy week had passed. Therefore, lays foundation designing refining movement-based protocols immersive environments.

Language: Английский

Citations

3