A Literature Survey on Potential Private User Information Leakage in Metaverse Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it