In recent years, eye-tracking data have been frequently used for precise modeling of user states, including cognitive load, physical load, and cybersickness in virtual reality (VR). However, these data can also expose sensitive biometric and behavioral signatures of the users that allow for re-identification and the inference of sensitive demographics via linkage attacks. Existing privacy-preserving methods (e.g., differential privacy, federated learning, data anonymization, etc.) often compromise the fidelity of user state estimation, creating a research gap in balancing privacy with application utility. To address this, we propose a novel model that mitigates privacy leakage from eye-tracking data while preserving high accuracy in user state prediction. Our approach ranks eye-tracking features by their contribution to prediction and applies selective perturbation to high-risk features. Experimental results show significant reductions in demographic inference accuracy (gender: 95.4% to 60.0%, race: 86.3% to 49.7%, age: 76.3% to 55.3%), with only a 9.7% average accuracy drop for cognitive load, physical load, and cybersickness classification. Compared to a local differential privacy baseline, our method achieves higher utility preservation and stronger demographic suppression, yielding improvements of 19.1%, 20.6%, and 18.3% for age, gender, and race, respectively. These findings validate the potential for privacy-aware modeling in VR systems and offer a scalable path toward ethical, secure, and high-performance deployment of eye-tracking technology in real-world applications.

Cybersickness remains a major challenge in virtual and mixed reality (VR/MR), yet existing methods primarily focus on predicting its onset without offering formal guarantees regarding its occurrence or effective mitigation. As VR/MR applications expand into safety-critical domains like healthcare, defense, verifiable safety assurances become essential to protect users from adverse physiological and psychological effects. This paper introduces a probabilistic verification framework leveraging Bayesian Networks (BN) to explicitly model the interactions among system parameters, human physiological responses, and cybersickness severity. Unlike deep learning approaches that lack interpretability and formal verification capabilities, the proposed BN model explicitly captures how environmental and system-level factors (e.g., luminance, spectral entropy, and image gradient complexity via HoG features) influence physiological responses (e.g., heart rate, reaction time, eye tracking), ultimately affecting cybersickness severity. By learning the joint probability distribution of these factors, our approach provides rigorous formal guarantees on cybersickness risk under specified operational conditions. If these guarantees are not met, automated adaptive adjustments are recommended to restore safe conditions. Experimental validation involving physiological and systemlevel data demonstrates that Bayesian Networks provide an interpretable and efficient framework, uniquely enabling formal probabilistic verification of cybersickness risks. This capability makes the proposed approach particularly suitable for designing and deploying VR/MR systems with explicitly verified safety constraints.

Extended Reality (XR) systems are gaining widespread usability due to their impressive adaptability capabilities; yet, the critical challenge they face is user privacy. The subtle behavioral patterns revealed through human motion in XR environments serve as significant indicators that can be used to infer user characteristics. Accordingly, gender classification utilizing behavioral motion data has emerged as a critical research focus in this field. Traditional machine learning models have achieved success using gait or inertial data in surveillance and mobile applications. However, there is still limited work that targets XR-specific motion signals captured through headsets, controllers, and body tracking devices. The proposed interpretable framework, "YouKnowWho," aims to address this challenge by developing an explainable model for classifying gender from behavioral motion data. The framework consists of two sequential deep learning architectures: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The performance of this framework is assessed using accuracy, precision, recall, and F1-score. Additionally, Integrated Gradients are used to analyze the importance of each feature in predicting gender. The results demonstrate that YouKnowWho is capable of classifying gender from behavioral motion data with a remarkable accuracy of 68.2%, and it can identify specific features that can be preserved or safeguarded to ensure user privacy.

Personal characteristics fundamentally shape virtual reality (VR) experiences, yet their integration into predictive models remains underexplored. This paper studies how to incorporate personal attributes (age, gender, prior VR experience) into Bayesian networks for cybersickness prediction via: (i) direct inclusion as root nodes, (ii) a two-stage model that learns a susceptibility score from personal attributes, and (iii) a stratified model. Using 26,040 samples from VR maze-navigation experiments, direct inclusion attains 82.53% accuracy (+14.02 percentage points over a 68.51% no-personal baseline). The two-stage approach reaches 77.32% while supporting cold-start prediction for unseen users, and stratified models achieve 73.62%. Using participant-level cross-validation to avoid subject leakage, we find that personalization consistently improves cybersickness prediction. These results argue that personal attributes should be treated as first-class signals in cybersickness models, with clear design trade-offs between maximal accuracy and deployability for unseen users, informing personalized VR systems and adaptive content delivery.

This study proposes a dielectric terahertz (THz) porous core fibre with ultra‐high birefringence and near zero dispersion‐flattened properties. The finite element method is used to design and analyse properties of the proposed THz fibre. The proposed porous core crystal fibre has a triangular lattice with microstructured circular air holes in the outer cladding and elliptical air holes in the core. The design exhibits a high birefringence of 7 × 10 −2 and a low effective material loss of 0.1 cm −1 at the operating frequency of f = 1 THz. It also shows nearly zero flattened dispersion with an absolute dispersion variation of ±0.05 ps/THz/cm in the frequency range of 1.3 to 2.2 THz. Power fraction and confinement loss are also reported in this study. The proposed THz porous fibre is deemed suitable for polarisation‐maintaining THz wave guidance.

In this paper we propose a system for unobtrusive automated indoor surveillance of subjects in indoor environment using the Kinect sensor. We demonstrate that the features of identity, location and activity of a person can be detected with considerable accuracy using the system. Further, we show how existing design patterns can be used to create a data parallel and scalable architecture for such surveillance in real-time.