In this article, we study the privacy and security aspects of the metaverse in the context of digital healthcare. Our studies include the security aspects of data collection and communications for access to the metaverse, the privacy and security threats of employing Machine Learning and Artificial Intelligence (AI/ML) algorithms for metaverse healthcare, and the privacy of social interactions among patients in the metaverse from a human-centric perspective. In this article, we aim to provide new perspectives and less-investigated solutions, which are shown to be promising mechanisms in the context of wireless communications and computer science and can be considered novel solutions to be applied to healthcare metaverse services. Topics include physical layer security (PHYSec), Semantic Metaverse Communications (SMC), Differential Privacy (DP), and Adversarial Machine Learning (AML). As a case study, we propose distributed differential privacy for the metaverse healthcare systems, where each virtual clinic perturbs its medical model vector to enhance privacy against malicious actors and curious servers. Through our experiments on the Breast Cancer Wisconsin Dataset (BCWD), we highlight the privacy-utility trade-off for different adjustable levels of privacy.

翻译：本文研究了数字医疗背景下元宇宙的隐私与安全方面。我们的研究包括：访问元宇宙时数据收集与通信的安全问题；在元宇宙医疗中应用机器学习和人工智能（AI/ML）算法所面临的隐私与安全威胁；以及从以人为本的角度出发，元宇宙中患者之间社交互动的隐私问题。本文旨在提供新的视角和较少被探索的解决方案，这些方案在无线通信和计算机科学领域已被证明是有前途的机制，并可作为创新解决方案应用于元宇宙医疗服务。主题包括物理层安全（PHYSec）、语义元通信（SMC）、差分隐私（DP）和对抗性机器学习（AML）。作为案例研究，我们为元宇宙医疗系统提出了分布式差分隐私方案，其中每个虚拟诊所对其医疗模型向量进行扰动，以增强针对恶意攻击者和好奇服务器的隐私保护。通过在威斯康星州乳腺癌数据集（BCWD）上的实验，我们突出了不同可调隐私水平下的隐私-效用权衡。