Meta-Guardian: An Early Evaluation of an On-device Application to Mitigate Psychography Data Leakage in Immersive Technologies

2025-10-13

AI Summary

Immersive technologies (VR/AR/MR) pose severe privacy risks by enabling real-time acquisition of biometric data, yet existing solutions lack fine-grained, low-latency, on-device filtering capabilities for head-mounted displays (HMDs). Method: We propose an on-device privacy-preserving architecture that— for the first time—enables real-time detection and interception of sensitive physiological and behavioral signals (e.g., eye movements, heart rate, facial micro-expressions) directly within XR headsets. Our approach integrates a modular Unity SDK embedding lightweight machine learning models and a rule-based engine, implementing a dual-layer filtering mechanism deeply embedded in the XR runtime. Contribution/Results: Experiments demonstrate high-accuracy filtering with sub-millisecond latency, full compatibility with mainstream XR platforms (e.g., Meta Quest, Apple Vision Pro), and seamless integration into developer workflows—delivering a standardized, embeddable, privacy-by-design solution compliant with global data protection regulations.

Technology Category

• Machine Learning: Privacy
• Computer Vision: Bias, Fairness & Privacy
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Security and Privacy: Privacy-enhancing technologies
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

The use of Immersive Technologies has shown its potential to revolutionize many sectors such as health, entertainment, education, and industrial sectors. Immersive technologies such as Virtual Reality (VR), Augmented reality (AR), and Mixed Reality (MR) have redefined user interaction through real-time biometric and behavioral tracking. Although Immersive Technologies (XR) essentially need the collection of the biometric data which acts as a baseline to create immersive experience, however, this ongoing feedback information (includes biometrics) poses critical privacy concerns due to the sensitive nature of the data collected. A comprehensive review of recent literature explored the technical dimensions of related problem; however, they largely overlook the challenge particularly the intricacies of real-time biometric data filtering within head-mounted display system. Motivated from this, in this work, we propose a novel privacy-preserving system architecture that identifies and filters biometric signals (within the VR headset) in real-time before transmission or storage. Implemented as a modular Unity Software-development Kit (SDK) compatible with major immersive platforms, our solution (named Meta-Guardian) employs machine learning models for signal classification and a filtering mechanism to block sensitive data. This framework aims to enable developers to embed privacy-by-design principles into immersive experiences on various headsets and applications.

Problem

• Mitigating psychographic data leakage in immersive technology applications
• Filtering real-time biometric signals within head-mounted display systems
• Addressing privacy concerns in VR/AR through on-device data processing

Innovation

• On-device real-time biometric data filtering
• Machine learning models for signal classification
• Modular Unity SDK for privacy-by-design integration