Healthcare systems increasingly rely on digital twin technology to create virtual representations of patients, medical devices, and clinical workflows. However, secure data sharing across medical digital twin edge networks faces critical privacy and security challenges when handling sensitive patient information and treatment protocols. This paper presents a medical digital twin blockchain sharding (MDTBS) framework that leverages blockchain sharding technology to address security and privacy concerns in healthcare data sharing while maintaining the real-time responsiveness required for clinical operations. The framework incorporates a novel dual-layer architecture combining local medical data sharing chains using directed acyclic graph consensus for intra-hospital communications with global medical data sharing chains employing delegated proof of stake consensus for inter-hospital collaboration. Considering the dynamic characteristics of medical environments and mapping errors between physical healthcare systems and their digital twins, we formulate an adaptive resource allocation model that jointly optimizes medical cluster head selection, hospital base station consensus access, and spectrum and computation resource allocation to maximize blockchain sharding transaction throughput. A medical digital twin edge network-empowered two-layer proximal policy optimization algorithm solves the complex optimization problems while adapting to time-varying medical workflows and equipment configurations. Simulation experiments demonstrate that the framework achieves superior performance across all evaluation metrics compared to baseline methods, including 15-25% improvements in transaction throughput with statistical significance (p-value less than 0.001), sub-three-second emergency response times, and 85%+ privacy preservation efficiency scores.
Zhao et al. (Thu,) studied this question.