PrivateXR: Defending Privacy Attacks in Extended Reality Through Explainable AI-Guided Differential Privacy

Selective privacy protection is more efficient than blanket approaches: Rather than adding privacy-protecting noise to all data features equally, the researchers found that targeting only the most influential features reduces privacy attacks (membership inference attacks by up to 43%, re-identification attacks by up to 39%) while maintaining model accuracy at 97% and improving inference speed—critical for real-time XR applications.

Explainable AI can guide privacy decisions: By using explainable AI techniques to identify which features most influence model predictions, the framework determines where privacy protection is most needed, allowing developers to apply privacy measures strategically rather than uniformly across all data.

Eye-tracking and biometric data in XR systems face serious privacy risks: The paper demonstrates that sensitive data commonly collected in extended reality systems (eye-tracking, emotion recognition, activity data) can be exploited through membership inference and re-identification attacks, highlighting why privacy protections are essential as XR adoption grows.

Privacy and performance don't have to be mutually exclusive: The research shows that implementing privacy protections through differential privacy doesn't necessarily require sacrificing model accuracy or real-time performance—key concerns for practical XR deployment—when privacy measures are applied intelligently rather than broadly.