A Blockchain-Integrated Multi-Server Queueing Framework for Privacy-Preserving Healthcare Systems Using Non-Fungible Tokens and Zero-Knowledge Proofs

Abstract

Blockchain-enabled healthcare infrastructures demand latency-aware, privacy-preserving, and scalable transaction management due to the stochastic and high-volume nature of clinical data processing in decentralized environments. In this study, we propose a blockchain-aware Modified M/M/C (Mo M/M/C) queueing framework specifically designed for NFT-enabled healthcare systems integrated with Zero-Knowledge Proof (ZKP)-based verification. Unlike classical queueing models that assume single-stage service, the proposed model incorporates a multi-stage transaction pipeline consisting of medical processing, NFT tokenization, ZKP verification, and blockchain validation. Accordingly, an effective service rate formulation and blockchain-adjusted arrival rate are analytically derived to capture cryptographic overhead, consensus latency, and smart contract execution delays within a unified stochastic framework. Patient records, prescriptions, and diagnostic data are securely encapsulated as NFTs to ensure immutability, traceability, and decentralized ownership, while ZKP protocols enable privacy-preserving authentication without exposing sensitive medical information. The model further integrates dynamic priority-aware scheduling and validation-aware utilization to optimize resource allocation under heterogeneous healthcare workloads. Extensive discrete-event simulations conducted over scalable transaction volumes (1,000–100,000) evaluate key performance metrics including throughput, average waiting time, system response time, and latency. The results demonstrate that the proposed Mo M/M/C framework significantly improves queue stability, reduces congestion, and enhances throughput compared to FIFO, LIFO, SIRO, and standard M/M/C models. Overall, the revised framework provides a mathematically grounded, cryptography-aware, and blockchain-consistent solution for secure and real-time healthcare transaction management