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 Journal Article
In: Advanced Intelligent Systems, vol. 8, no. 1, 2026, ISSN: 26404567 (ISSN).
Abstract | Links | BibTeX | Tags: 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.
Dang, L. B.; Truong, V. Tuan; Le, L. B.; Falk, T. H.
EM-Auth: A Generalizable Eye-Movement Authentication Framework using Event Cameras Proceedings Article
In: IEEE Conf. Artif. Intell., CAI, pp. 2064–2069, Institute of Electrical and Electronics Engineers Inc., 2026, ISBN: 979-833156039-3 (ISBN), (Journal Abbreviation: IEEE Conf. Artif. Intell., CAI).
Abstract | Links | BibTeX | Tags: augmented reality, Authentication, Cameras, Convolutional neural networks, Data privacy, Eye movement patterns, Eye movements, eye tracking, Eye-tracking, Frame-based, High power consumption, Neuromorphic vision sensors, Power potential, Privacy concerns, Temporal resolution, Transfer learning, User authentication, Virtual and augmented reality, virtual reality
@inproceedings{dangEMAuthGeneralizableEyeMovement2026,
title = {EM-Auth: A Generalizable Eye-Movement Authentication Framework using Event Cameras},
author = {L. B. Dang and V. Tuan Truong and L. B. Le and T. H. Falk},
url = {https://www.scopus.com/pages/publications/105042114237?origin=resultslist},
doi = {10.1109/CAI68641.2026.11536355},
isbn = {979-833156039-3 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {IEEE Conf. Artif. Intell., CAI},
pages = {2064–2069},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {With the rapid emergence of virtual and augmented reality (VR/AR) head-mounted devices that closely interact with users' eyes, eye-tracking-based applications are becoming increasingly promising. Among them, eye movement patterns have emerged as a reliable biometric modality for user authentication. However, existing solutions based on conventional frame-based cameras face significant limitations, including low temporal resolution, high latency, high power consumption, and potential privacy concerns due to the detailed appearance captured around the eyes. To address these challenges, we propose EM-Auth, a novel framework for eye-tracking-based user authentication that leverages event cameras, a type of neuromorphic vision sensor providing asynchronous, high-temporal-resolution data, while preserving user privacy. Our method introduces a two-stage spatiotemporal feature extraction pipeline: first, a lightweight convolutional neural network (CNN) extracts spatial features from event streams; second, a transformer-based module models the temporal dependencies to generate robust eye representations. Extensive experiments show that our framework significantly outperforms existing state-of-the-art (SOTA) methods, achieving 0.008 and 0.015 equal error rates (EERs) on two representative datasets. Notably, EM-Auth demonstrates a strong generalization capability. In a zero-shot setting where the model is trained on a specific dataset and evaluated on previously unseen users, our approach achieves authentication accuracies of 98.4% and 95.9% on the EV-Eye and EBV-Eye datasets, respectively. Under cross-dataset evaluation, our framework achieves an authentication accuracy of nearly 85% without additional fine-tuning, and consistently improves performance through transfer learning. © 2026 IEEE.},
note = {Journal Abbreviation: IEEE Conf. Artif. Intell., CAI},
keywords = {augmented reality, Authentication, Cameras, Convolutional neural networks, Data privacy, Eye movement patterns, Eye movements, eye tracking, Eye-tracking, Frame-based, High power consumption, Neuromorphic vision sensors, Power potential, Privacy concerns, Temporal resolution, Transfer learning, User authentication, Virtual and augmented reality, virtual reality},
pubstate = {published},
tppubtype = {inproceedings}
}
With the rapid emergence of virtual and augmented reality (VR/AR) head-mounted devices that closely interact with users' eyes, eye-tracking-based applications are becoming increasingly promising. Among them, eye movement patterns have emerged as a reliable biometric modality for user authentication. However, existing solutions based on conventional frame-based cameras face significant limitations, including low temporal resolution, high latency, high power consumption, and potential privacy concerns due to the detailed appearance captured around the eyes. To address these challenges, we propose EM-Auth, a novel framework for eye-tracking-based user authentication that leverages event cameras, a type of neuromorphic vision sensor providing asynchronous, high-temporal-resolution data, while preserving user privacy. Our method introduces a two-stage spatiotemporal feature extraction pipeline: first, a lightweight convolutional neural network (CNN) extracts spatial features from event streams; second, a transformer-based module models the temporal dependencies to generate robust eye representations. Extensive experiments show that our framework significantly outperforms existing state-of-the-art (SOTA) methods, achieving 0.008 and 0.015 equal error rates (EERs) on two representative datasets. Notably, EM-Auth demonstrates a strong generalization capability. In a zero-shot setting where the model is trained on a specific dataset and evaluated on previously unseen users, our approach achieves authentication accuracies of 98.4% and 95.9% on the EV-Eye and EBV-Eye datasets, respectively. Under cross-dataset evaluation, our framework achieves an authentication accuracy of nearly 85% without additional fine-tuning, and consistently improves performance through transfer learning. © 2026 IEEE.