Metaverse-enabled digital healthcare systems are expected to exploit an unprecedented amount of personal health data, while ensuring that sensitive or private information of individuals are not disclosed. Machine learning and artificial intelligence (ML/AI) techniques can be widely utilized in metaverse healthcare systems, such as virtual clinics and intelligent consultations. In such scenarios, the key challenge is that data privacy laws might not allow virtual clinics to share their medical data with other parties. Moreover, clinical AI/ML models themselves carry extensive information about the medical datasets, such that private attributes can be easily inferred by malicious actors in the metaverse (if not rigorously privatized). In this paper, inspired by the idea of "incognito mode", which has recently been developed as a promising solution to safeguard metaverse users' privacy, we propose global differential privacy for the distributed metaverse healthcare systems. In our scheme, a randomized mechanism in the format of artificial "mix-up" noise is applied to the federated clinical ML/AI models before sharing with other peers. This way, we provide an adjustable level of distributed privacy against both the malicious actors and honest-but curious metaverse servers. Our evaluations on breast cancer Wisconsin dataset (BCWD) highlight the privacy-utility trade-off (PUT) in terms of diagnosis accuracy and loss function for different levels of privacy. We also compare our private scheme with the non-private centralized setup in terms of diagnosis accuracy.

翻译：元宇宙驱动的数字医疗系统预计将利用前所未有的个人健康数据量，同时确保不泄露个人的敏感或隐私信息。机器学习和人工智能（ML/AI）技术可广泛应用于元宇宙医疗系统，例如虚拟诊所和智能问诊。在此类场景中，关键挑战在于数据隐私法律可能不允许虚拟诊所与其他方共享其医疗数据。此外，临床AI/ML模型本身携带大量关于医疗数据集的详细信息，若未严格进行隐私保护，恶意行为者可在元宇宙中轻易推断出私有属性。本文受近期提出的“隐身模式”（incognito mode）这一保护元宇宙用户隐私的前沿方案启发，提出面向分布式元宇宙医疗系统的全局差分隐私。在我们的方案中，在联邦临床ML/AI模型与其他节点共享之前，对其应用一种以人工“混淆”噪声形式实现的随机化机制。通过这种方式，我们为抵御恶意行为者和诚实但好奇的元宇宙服务器提供了可调节的分布式隐私保护级别。我们在乳腺癌威斯康星数据集（BCWD）上的评估结果，展示了不同隐私级别下诊断准确率与损失函数所体现的隐私-效用权衡（PUT）。此外，我们还将所提出的隐私方案与非隐私的集中式方案在诊断准确率方面进行了比较。