부산대학교 도서관

In recent years, the healthcare industry has undergone a digital transformation, making patient data publicly available and accessible. Healthcare units make a portion of the data public while keeping the rest private, necessitating various mechanisms for security and privacy. Blockchain technology has been widely adopted in the healthcare sector to secure data transactions. However, public blockchains face challenges in scalability and privacy, whereas private blockchains struggle with centralization, interoperability, and complexity. To address these challenges, we propose an Internet of Medical Things (IoMT)-based hybrid blockchain architecture. The proposed architecture combines the decentralized Ethereum and the centralized Hyperledger Fabric blockchain (Eth-Fab) using SQLite to leverage Ethereum smart contracts with the Hyperledger permission model. Moreover, we introduce access control strategies to enhance patient data authentication and authorization. We have employed machine learning algorithms to assist healthcare practitioners in accurately detecting diseases and making time-efficient decisions. Additionally, we modeled the proposed architecture using the M/M/1 queuing model and derived closed-form expressions for latency, throughput, and server utilization. The validity of these expressions was verified through Monte Carlo simulations. The results demonstrate that higher service times (block generation) yield better outcomes in terms of latency, throughput, and utilization, regardless of the arrival time, i.e., transactions in the mining pool.