Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Jaberi, M.; Falk, T. H.
A Literature Survey on Potential Private User Information Leakage in Metaverse Applications Article de journal
Dans: Advanced Intelligent Systems, vol. 8, no 1, 2026, ISSN: 26404567 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: age, augmented reality, Brain, brain computer interface, Computer privacy, Data privacy, Electroencephalography, eye tracking, Gender, Haptic interfaces, Information leakage, Intelligent systems, Literature survey, metaverse, Metaverses, Neurophysiological signal, neurophysiological signals, Neurophysiology, Privacy, Private user information, Race, Signal analysis, User information, Virtual and augmented reality
@article{jaberiLiteratureSurveyPotential2026,
title = {A Literature Survey on Potential Private User Information Leakage in Metaverse Applications},
author = {M. Jaberi and T. H. Falk},
url = {https://www.scopus.com/pages/publications/105012929167?origin=resultslist},
doi = {10.1002/aisy.202500263},
issn = {26404567 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {Advanced Intelligent Systems},
volume = {8},
number = {1},
publisher = {John Wiley and Sons Inc},
abstract = {The Metaverse is revolutionizing various fields, including healthcare, education, social interaction, and the workplace. Commercial multisensory devices (e.g., smell diffusion and haptic technologies) are available, and virtual and augmented reality (VR/AR) headsets are increasingly integrated with brain–computer interfaces (BCI). These integrations enable adaptive, personalized virtual immersive experiences that are more engaging, interactive, and effective. As these applications become mainstream, concerns arise regarding the security and privacy of personal information. Recent studies demonstrate that users can be identified with high accuracy using the data monitored from sensors available in VR/AR headsets. This literature survey investigates the types of personal user information that can be inferred from BCI-instrumented headsets. In particular, it focuses on predicting age, gender, and ethnic/racial background from neurophysiological signals currently monitored by commercial devices. The survey highlights the predictive strength of electroencephalogram and electrocardiogram signal modalities, followed by eye tracking and iris scanning. It also considers future privacy risks posed by biometric and gesture-based monitoring using non-contact technologies such as computer vision and WiFi signal analysis. The survey concludes with recommendations for future research aimed at contributing to the development of robust frameworks that safeguard user privacy in the evolving Metaverse landscape. © 2025 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH.},
keywords = {age, augmented reality, Brain, brain computer interface, Computer privacy, Data privacy, Electroencephalography, eye tracking, Gender, Haptic interfaces, Information leakage, Intelligent systems, Literature survey, metaverse, Metaverses, Neurophysiological signal, neurophysiological signals, Neurophysiology, Privacy, Private user information, Race, Signal analysis, User information, Virtual and augmented reality},
pubstate = {published},
tppubtype = {article}
}
The Metaverse is revolutionizing various fields, including healthcare, education, social interaction, and the workplace. Commercial multisensory devices (e.g., smell diffusion and haptic technologies) are available, and virtual and augmented reality (VR/AR) headsets are increasingly integrated with brain–computer interfaces (BCI). These integrations enable adaptive, personalized virtual immersive experiences that are more engaging, interactive, and effective. As these applications become mainstream, concerns arise regarding the security and privacy of personal information. Recent studies demonstrate that users can be identified with high accuracy using the data monitored from sensors available in VR/AR headsets. This literature survey investigates the types of personal user information that can be inferred from BCI-instrumented headsets. In particular, it focuses on predicting age, gender, and ethnic/racial background from neurophysiological signals currently monitored by commercial devices. The survey highlights the predictive strength of electroencephalogram and electrocardiogram signal modalities, followed by eye tracking and iris scanning. It also considers future privacy risks posed by biometric and gesture-based monitoring using non-contact technologies such as computer vision and WiFi signal analysis. The survey concludes with recommendations for future research aimed at contributing to the development of robust frameworks that safeguard user privacy in the evolving Metaverse landscape. © 2025 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH.
2.
Jaberi, M.; Bouchard, S.; Falk, T. H.
Quantifying the Risk of Private Information Leakage in the Metaverse with EEG-Instrumented Virtual Reality Headsets Article d'actes
Dans: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern., p. 4293–4298, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 1062922X (ISSN); 979-833153358-8 (ISBN), (Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.).
Résumé | Liens | BibTeX | Étiquettes: Biomedical signal processing, Electroencephalography, Electrophysiology, Immersive, Information leakage, Interactivity, Learning systems, Metaverses, Neurophysiology, Privacy risks, Private information, Real-time quality, User engagement, virtual reality, Virtual reality technology, Virtual-reality headsets
@inproceedings{jaberiQuantifyingRiskPrivate2025,
title = {Quantifying the Risk of Private Information Leakage in the Metaverse with EEG-Instrumented Virtual Reality Headsets},
author = {M. Jaberi and S. Bouchard and T. H. Falk},
url = {https://www.scopus.com/pages/publications/105033158377?origin=resultslist},
doi = {10.1109/SMC58881.2025.11343511},
isbn = {1062922X (ISSN); 979-833153358-8 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
pages = {4293–4298},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {As virtual reality (VR) technologies become more immersive and metaverse applications burgeon, modern headsets are increasingly becoming equipped with sensors capable of capturing multiple neurophysiological signals. While these signals can be used to measure, in real-time, quality of experience metrics that can be used to enhance interactivity and user engagement, they may also introduce novel privacy risks by unintentionally leaking sensitive personal attributes. In this paper, we explore the extent in which electroencephalography (EEG) signals, recorded during an immersive VR memory task, can be used to infer users' private information, such as age, biological sex, and identity. We employ both classical machine learning models with hand-crafted features, as well as end-to-end deep learning approaches. Our findings demonstrate that EEG-based features can, indeed, leak information about biological sex, age, and user identity, with end-to-end models obtaining the best performance. Feature importance ranking and deep neural network saliency maps were used to provide explainability on the neural patterns used by the models. We conclude with recommendations on how these findings can also be used to help secure future metaverse applications. © 2025 IEEE.},
note = {Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
keywords = {Biomedical signal processing, Electroencephalography, Electrophysiology, Immersive, Information leakage, Interactivity, Learning systems, Metaverses, Neurophysiology, Privacy risks, Private information, Real-time quality, User engagement, virtual reality, Virtual reality technology, Virtual-reality headsets},
pubstate = {published},
tppubtype = {inproceedings}
}
As virtual reality (VR) technologies become more immersive and metaverse applications burgeon, modern headsets are increasingly becoming equipped with sensors capable of capturing multiple neurophysiological signals. While these signals can be used to measure, in real-time, quality of experience metrics that can be used to enhance interactivity and user engagement, they may also introduce novel privacy risks by unintentionally leaking sensitive personal attributes. In this paper, we explore the extent in which electroencephalography (EEG) signals, recorded during an immersive VR memory task, can be used to infer users' private information, such as age, biological sex, and identity. We employ both classical machine learning models with hand-crafted features, as well as end-to-end deep learning approaches. Our findings demonstrate that EEG-based features can, indeed, leak information about biological sex, age, and user identity, with end-to-end models obtaining the best performance. Feature importance ranking and deep neural network saliency maps were used to provide explainability on the neural patterns used by the models. We conclude with recommendations on how these findings can also be used to help secure future metaverse applications. © 2025 IEEE.