Providing Privacy for Eye-Tracking Data With Applications in XR

Area of Science:

• Computer Science
• Human-Computer Interaction
• Cybersecurity

Background:

• Eye tracking is a key technology for extended reality (XR), enabling advanced features like efficient rendering and natural interaction.
• However, eye-tracking data poses significant privacy risks, including the potential for biometric identity theft and sensitive behavioral information leakage.

Purpose of the Study:

• To develop and evaluate privacy-preserving techniques for the entire eye-tracking data pipeline.
• To demonstrate that privacy and system functionality can coexist in XR environments.

Main Methods:

• Protection of biometric data at the sensor level.
• Securing real-time gaze data streams.
• Application of formal privacy guarantees to collected datasets.

Main Results:

• Developed novel privacy-preserving techniques applicable across the eye-tracking pipeline.
• Demonstrated that implementing privacy measures does not compromise the functionality of eye-tracking systems.
• Validated the feasibility of privacy-by-design approaches for XR.

Conclusions:

• Privacy and functionality are achievable simultaneously in eye-tracking systems for XR.
• Privacy-by-design strategies are crucial for the secure development of future XR systems and platforms.
• This research provides a foundational framework for privacy-preserving eye-tracking in XR.