Privacy-Enhanced Healthcare Monitoring Service Refreshment in Human Digital Twin-Assisted Fabric Metaverse

Human digital twin bridges humans with digital avatars in the fabric metaverse, assisting users and healthcare professionals with real-time visualization, analysis, and prediction of personal data sensed by fabric sensors. The human digital twin-assisted healthcare monitoring (HHM) service refreshment refers to sending personal health data to corresponding services hosted on nearby edge servers and receiving the results to update local digital avatars continuously. However, the malicious nature and resource limitations of edge servers may lead to user privacy leaks and refreshment timeout, thereby impacting diagnostics. In this paper, we investigate a novel privacy-enhanced HHM service refreshment maximization problem in the fabric metaverse by considering privacy data encryption, model compression, and personalized user requirements. To this end, we first formulate the above issue as an Integer Linear Programming (ILP) problem, and prove its NP-hardness. Then, a resource scheduler named Wiper is designed, consisting of a shallow-deep distiller and an agile refresher library. To enable efficient inference while preserving user privacy, the former replaces violation modules in existing models with approximations and conducts shallow distillation on model layers to meet operation type and depth limits of homomorphic encryption, and then deep distillation on model parameters to decrease end-to-end refreshment delay. Finally, to satisfy user requirements on accuracy and delay during encrypted refreshments while maximizing the throughput of HHM services in offline and online situations with different problem scales, a series of HHM service refreshment algorithms are merged into the latter, including exact, performance-guaranteed approximation, and residual diffusion reinforcement learning algorithms. Theoretical analyses and experiments demonstrate that our algorithms are promising compared with baseline algorithms. © 2025 IEEE.