Data Supervision Scheme of Medical Centralized Purchase Based on Multi-Chain Collaboration

The wide application of blockchain in the medical field has gradually brought attention to the effective supervision of diversified, sensitive, and continuously growing medical centralized purchasing data. However, owing to complex business relationships, existing medical centralized purchase data supervision schemes are not efficient in multi-department collaborative supervision processes, and shared supervision data carry the risk of privacy disclosure. Therefore, based on multi-chain collaboration, this paper proposes a supervision scheme for medical centralized purchase data, supporting security sharing. This scheme constructs a multi-chain collaborative supervision framework based on the supervision relay chain, summarizes the supervision elements and supervised data objects for the medical centralized purchase business, and forms a comprehensive view of multi-chain collaborative supervision and cross-chain interaction. Multiple regulatory information flows are described through the multi-chain collaborative regulatory model, and the regulatory elements are described as a structured list of cross-sectoral comprehensive regulatory matters to support multi-sector, multi-link, and cross-chain supervision. During the implementation of multi-chain supervision, a large amount of regulated data generated by the medical centralized purchase business is symmetrically encrypted and stored in an Inter Planetary File System (IPFS), reducing the storage burden of blockchain. Proxy Re-Encryption (PRE) technology is introduced to ensure the secure sharing of symmetric key and metadata between multiple chains, and a searchable encryption algorithm is integrated to support the retrieval of the IPFS file address ciphertext from the chain. By analyzing the medical centralized purchase business, a security analysis of the process flow of data supervision is carried out. Then, the performance of chain codes such as authorization, upload, and query in the collaborative supervision process is evaluated. Experimental results show that the proposed scheme is secure and efficient, is more suitable for the medical field than similar schemes, and meets the requirements of multi-sector and multi-link collaborative supervision and data security sharing.