Quantifying the Risk of Private Information Leakage in the Metaverse with EEG-Instrumented Virtual Reality Headsets
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".