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.

翻译：元宇宙作为下一代互联网的核心，是一个由计算机生成的、融合时空性、沉浸感、实时性、可持续性、互操作性与数据敏感性的全息数字环境。它巧妙融合虚实世界，让用户能够以虚拟形态进行创作、社交与交易。随着增强现实、虚拟现实及区块链等新兴技术的快速发展，元宇宙系统日趋完善，广泛应用于社交、旅游、工业和经济等领域。然而，其与真实世界的高度交互性也意味着个人和企业面临巨大的隐私泄露风险，这阻碍了元宇宙的广泛部署。因此，在元宇宙框架中应用隐私计算技术势在必行，这已成为当前的研究热点。本文对隐私计算与元宇宙相遇时的必要性、分类体系及挑战进行了全面研究。具体而言，我们首先介绍了元宇宙的基础技术与各类应用场景，并据此分析了元宇宙中数据使用的挑战，尤其是数据隐私问题。接着，我们针对元宇宙中的不同隐私问题，综述并归纳了基于联邦学习、差分隐私、同态加密及零知识证明的最新解决方案。最后，我们展示了元宇宙当前面临的安全与隐私挑战，并为构建完善的隐私保护元宇宙系统提供了开放研究方向。为便于查阅与引用，我们将相关文献及其代码整合至GitHub仓库：https://github.com/6lyc/Awesome-Privacy-Computing-in-Metaverse.git。