Supply chain finance, a critical tool for industrial chain coordination in the digital economy, faces challenges such as centralized identity authentication, data silos, and privacy risks, with traditional models constrained by single-point failures and inefficient data sharing. While blockchain's decentralized and tamper-proof features offer a solution, existing approaches often lack comprehensiveness. To address this, this study proposes two integrated solutions: first, the ZK-SCFI scheme, which leverages Merkle trees for identity storage, Paillier homomorphic encryption for pseudo-identity generation, and zero-knowledge proofs for privacy-preserving verification, effectively avoiding centralized risks and ensuring transaction non-associability; second, the TRU-SABE framework, combining blockchain and IPFS to enhance ciphertext-policy attribute-based encryption (CP-ABE) with keyword search, user revocation, outsourced decryption, and malicious user tracking, addressing traditional limitations like high computational costs and scalability issues. Experiments demonstrate TRU-SABE's superior efficiency, with user-side decryption overhead reduced to 3TE and storage advantages from factor group structures for attribute keys and ciphertexts, while the hybrid architecture alleviates data silos and storage pressure. A prototype system built on Fisco Bcos consortium blockchain, Spring Boot backend, and Vue.js frontend validates core functionalities, including encrypted data sharing and smart contract-based traceability. This work provides a holistic privacy-preserving solution for supply chain finance, with future directions focusing on balancing privacy with regulatory compliance, optimizing multi-authority key management, and expanding system capabilities to enhance security and efficiency.

Xin Cui. Research on Identity Authentication Algorithm for Supply Chain Finance Based on Zero Knowledge Proof and Blockchain. International Journal of Business Management and Economics and Trade (2025), Vol. 6, Issue 1: 138-148. https://doi.org/10.38007/IJBMET.2025.060114.

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