Privacy Computing Meets Metaverse: Necessity, Taxonomy and Challenges

TL;DR

This paper addresses the privacy risks inherent in the metaverse, a space augmented by XR, digital twins, and blockchain, by providing a comprehensive survey of privacy-preserving techniques. It introduces a taxonomy of privacy computing approaches—Federated Learning, Differential Privacy, Secure Multi-Party Computing, and Trusted Execution Environments—and analyzes their applicability across metaverse domains such as social, tourism, industry, and virtual economy. The authors identify key data-usage challenges, propose blended privacy solutions, and distill lessons learned, emphasizing that no single technique suffices and that hybrid, endogenous security architectures are essential. The work also outlines future directions, including AIGC governance, lightweight privacy tech, and decentralized security mechanisms, and provides a GitHub resource to facilitate practical adoption and collaboration.

Abstract

Metaverse, the core of the next-generation Internet, is a computer-generated holographic digital environment that simultaneously combines spatio-temporal, immersive, real-time, sustainable, interoperable, and data-sensitive characteristics. It cleverly blends the virtual and real worlds, allowing users to create, communicate, and transact in virtual form. With the rapid development of emerging technologies including augmented reality, virtual reality and blockchain, the metaverse system is becoming more and more sophisticated and widely used in various fields such as social, tourism, industry and economy. However, the high level of interaction with the real world also means a huge risk of privacy leakage both for individuals and enterprises, which has hindered the wide deployment of metaverse. Then, it is inevitable to apply privacy computing techniques in the framework of metaverse, which is a current research hotspot. In this paper, we conduct comprehensive research on the necessity, taxonomy and challenges when privacy computing meets metaverse. Specifically, we first introduce the underlying technologies and various applications of metaverse, on which we analyze the challenges of data usage in metaverse, especially data privacy. Next, we review and summarize state-of-the-art solutions based on federated learning, differential privacy, homomorphic encryption, and zero-knowledge proofs for different privacy problems in metaverse. Finally, we show the current security and privacy challenges in the development of metaverse and provide open directions for building a well-established privacy-preserving metaverse system. For easy access and reference, we integrate the related publications and their codes into a GitHub repository: https://github.com/6lyc/Awesome-Privacy-Computing-in-Metaverse.git.

Figures (3)

• Figure 1: Key technologies in the metaverse.
• Figure 2: Metaverse Applications and Privacy Concerns.
• Figure 3: The overview of privacy computing in the metaverse. ① is the schematic diagram of Metaverse. It relies on various terminals, such as VR and AR, to collect user personal information and environmental data, and then store them on the server. The server analyzes and processes this information, and then transmits feedback to the terminal device. ② is the schematic diagram of Federated Learning ($\nabla g_\theta^i$ represents the model gradient of each client). ③ is the schematic diagram of Differential Privacy. ④ is the schematic diagram of Homomorphic Encryption. The letters M and R indicate Message (i.e. key and sensitive data) and Result respectively. ⑤ is the schematic diagram of Zero-Knowledge Proofs. ⑥ is the chematic diagram of Trusted Execution Environment. These five privacy computing technologies constitute a comprehensive privacy-preserving system for the metaverse. The interconnectedness of these components lies in their collective contribution to a privacy-preserving system. Data is protected through decentralized training (FL), additional noise (DP), encryption (HE), proofs without revelation (ZKPs), and secure execution (TEE), ensuring that users can interact within the metaverse while minimizing the risk of privacy breaches.