  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.
