[Paper Review] Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey

Mahdi Alkaeed, Adnan Qayyum+1 more|arXiv (Cornell University)|Sep 19, 2023

Privacy-Preserving Technologies in Data4 citations

TL;DR

This survey investigates privacy challenges in AI-XR-enabled metaverses, where fine-grained user data collection for immersive experiences raises significant risks. It proposes technical solutions like Homomorphic Encryption, Differential Privacy, Federated Learning, and interpretable AI, demonstrating that combining privacy-enhancing technologies with human-centric design can mitigate privacy threats and enhance user trust in the metaverse.

ABSTRACT

The metaverse is a nascent concept that envisions a virtual universe, a collaborative space where individuals can interact, create, and participate in a wide range of activities. Privacy in the metaverse is a critical concern as the concept evolves and immersive virtual experiences become more prevalent. The metaverse privacy problem refers to the challenges and concerns surrounding the privacy of personal information and data within Virtual Reality (VR) environments as the concept of a shared VR space becomes more accessible. Metaverse will harness advancements from various technologies such as Artificial Intelligence (AI), Extended Reality (XR), Mixed Reality (MR), and 5G/6G-based communication to provide personalized and immersive services to its users. Moreover, to enable more personalized experiences, the metaverse relies on the collection of fine-grained user data that leads to various privacy issues. Therefore, before the potential of the metaverse can be fully realized, privacy concerns related to personal information and data within VR environments must be addressed. This includes safeguarding users' control over their data, ensuring the security of their personal information, and protecting in-world actions and interactions from unauthorized sharing. In this paper, we explore various privacy challenges that future metaverses are expected to face, given their reliance on AI for tracking users, creating XR and MR experiences, and facilitating interactions. Moreover, we thoroughly analyze technical solutions such as differential privacy, Homomorphic Encryption (HE), and Federated Learning (FL) and discuss related sociotechnical issues regarding privacy.

Motivation & Objective

• Address the growing privacy risks in AI-XR metaverses due to extensive collection of biometric, behavioral, and personal data.
• Identify technical and sociotechnical challenges in preserving user privacy within immersive virtual environments.
• Evaluate privacy-enhancing technologies (PETs) such as Homomorphic Encryption, Differential Privacy, and Federated Learning for application in metaverse systems.
• Examine the role of interpretable and transparent AI models in enhancing user control, consent, and trust in data processing.
• Propose a human-centric AI framework to align metaverse development with ethical principles and user well-being.

Proposed method

• Systematically analyze privacy threats in AI-XR metaverses, including data leakage, tracking, and inference attacks.
• Survey and categorize privacy-preserving technologies: Homomorphic Encryption (HE) for computation on encrypted data, Differential Privacy (DP) for noise-based anonymization, and Federated Learning (FL) for decentralized model training.
• Assess edge and embedded machine learning as mechanisms to keep sensitive data on-device, reducing transmission and exposure risks.
• Integrate human-centric AI principles into metaverse design, emphasizing user control, transparency, and ethical stakeholder collaboration.
• Evaluate the black-box nature of deep learning models and advocate for inherently interpretable models to improve auditability and user understanding.
• Propose a multidisciplinary framework combining technical PETs, regulatory policies, and ethical AI practices to secure the metaverse ecosystem.

Experimental results

Research questions

• RQ1 What are the primary privacy threats arising from AI and XR technologies in metaverse environments?
• RQ2 How can Homomorphic Encryption, Differential Privacy, and Federated Learning be effectively applied to protect user data in AI-XR metaverses?
• RQ3 In what ways does the black-box nature of deep learning models compromise user privacy and trust in the metaverse?
• RQ4 How can a human-centric AI approach improve privacy, consent, and user autonomy in immersive virtual environments?
• RQ5 What are the key technical and sociotechnical challenges in deploying privacy-preserving technologies in real-world metaverse systems?

Key findings

• The metaverse's reliance on fine-grained data—such as biometrics, location, and behavioral patterns—creates high-risk attack surfaces for privacy breaches.
• Homomorphic Encryption enables computation on encrypted data, preserving privacy during processing, though it incurs high computational overhead.
• Differential Privacy provides formal privacy guarantees by adding calibrated noise to data or model outputs, effectively limiting re-identification risks.
• Federated Learning allows model training across decentralized devices without sharing raw data, significantly reducing data exposure while maintaining model utility.
• Edge and embedded machine learning reduce data transmission by processing information locally, enhancing privacy and reducing latency.
• Interpretable and transparent AI models improve user trust by enabling users to understand, audit, and control how their data is used in metaverse applications.