Blockchain-Based Multi-Chain Data Supervision Mechanism for Traditional Chinese Medicine Traceability System

Abstract: Addressing the challenges of Traditional Chinese Medicine (TCM) traceability systems, including heavy data storage burdens, poor privacy protection, and susceptibility to tampering, this study establishes a highly secure and trustworthy traceability supervision system for the entire Chinese medicine supply chain, which enhances product quality and safety assurance. Centred on the Hyperledger Fabric consortium blockchain as its core architecture, a multi-chain integration framework comprising one regulatory main chain plus five organisational sub-chains is proposed to achieve permission control, data isolation, and privacy. A multi-mode encrypted data storage mechanism is designed, integrating China’s national cryptographic algorithms SM4 and SM3 with CP-ABE attribute-based encryption to enable tiered management of private and non-private data. Zero-knowledge proof technology safeguards identity privacy during cross-chain data transmission, while QR codes and environmental data collection mechanisms enhance data entry efficiency and authenticity. The system achieves end-to-end traceability from cultivation and processing through transportation, warehousing, and sales. Comparative performance analysis shows that the proposed framework effectively alleviates data storage pressure, ensures data validity, enhances data security, and improves collaborative efficiency among organizations across the TCM supply chain. The proposed multi-chain integrated Chinese medicine traceability and supervision system enables efficient collaboration and trustworthy traceability across the entire Chinese medicine industry chain, while safeguarding data security and privacy, and has significant application and promotion value. Future integration with artificial intelligence and big data technologies could further enhance the system’s intelligent analysis and decision-support capabilities.

Rongjun Chen, Yun Sun, Feng Xue, Yongzhi Ma, Xinyu Wu, Xianxian Zeng, Jiawen Li and Jinchang Ren. “Blockchain-Based Multi-Chain Data Supervision Mechanism for Traditional Chinese Medicine Traceability System”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.2 (2026). http://dx.doi.org/10.14569/IJACSA.2026.01702100

@article{Chen2026,
title = {Blockchain-Based Multi-Chain Data Supervision Mechanism for Traditional Chinese Medicine Traceability System},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.01702100},
url = {http://dx.doi.org/10.14569/IJACSA.2026.01702100},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {2},
author = {Rongjun Chen and Yun Sun and Feng Xue and Yongzhi Ma and Xinyu Wu and Xianxian Zeng and Jiawen Li and Jinchang Ren}
}