Obscuring the 'Who,' Preserving the 'What': Targeted Eye-tracking Feature Obfuscation in Virtual Reality for Privacy-Utility Balance

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.