@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}

}