Medishuffle: Auditable Coin Mixing Scheme for Internet of Medical Things

Yuan Chang, Tom H. Luan, Yinbin Miao, Qian Chen

Abstract: The widespread adoption of the Internet of Medical Things (IoMT) has spurred the development of blockchain-based models for medical data sharing, designed to ensure both data trustworthiness and privacy protection. In these models, data owners upload medical data to the blockchain via anonymized transactions, enabling researchers to access and analyze the on-chain data. Existing approaches often utilize coin-mixing techniques to obscure the relationships between transacting parties on the blockchain, thereby enhancing privacy. However, such solutions face challenges related to inefficiency and limited data usability, making them unsuitable for deployment in blockchain-based medical scenarios. To address the issue, we propose Medishuffle, an auditable coin-mixing protocol tailored for IoMT applications. Medishuffle introduces a polynomial-based group signature algorithm to facilitate the shuffling process, leveraging identity anonymity instead of traditional data encryption to reduce computational overhead. Additionally, the traceability feature of group signatures enables auditors to reconstruct the sequence of obfuscated transactions when necessary. Through theoretical analysis, we demonstrate that Medishuffle ensures both the unforgeability and traceability of signatures under the random oracle model in the query process. Using experiments, we show that Medishuffle outperforms existing solutions by offering enhanced functionality without a significant increase in computational overhead, making it a practical and efficient solution for IoMT-based medical data sharing.

External IDs:dblp:journals/iotj/ChangLMC25