Vegetable IoT Blockchain Anti Counterfeiting Traceability System Based on PQ-ECIES

The vegetable supply chain is characterized by multiple production entities, diverse product varieties, and complex circulation processes, which often result in low data accuracy, label forgery, data tampering, and difficulties in cross-enterprise collaboration in traditional traceability systems. Furthermore, the rapid development of quantum computing poses significant threats to existing cryptographic foundations by enabling efficient factorization or discrete logarithm attacks. This study aimed to design and implement a vegetable supply chain anti-counterfeiting and traceability system that integrates the Internet of Things (IoT), blockchain technology, and a post-quantum enhanced elliptic curve integrated encryption scheme (PQ-ECIES). The system seeks to enhance the trustworthiness, privacy protection, and collaborative efficiency of supply chain data management, while maintaining practical performance for IoT devices and high-frequency data uploading scenarios.

The proposed system was constructed on an IoT framework incorporating nine categories of devices. A registration and admission mechanism was developed to establish a trusted mapping between "device–enterprise–data", effectively preventing unauthorized entities from uploading forged data. At the data layer, collected information was divided into public and private categories: Public data were uploaded directly to the blockchain, while private data were encrypted using PQ-ECIES before being stored on-chain. Smart contracts automated processes such as data classification, permission verification, and encrypted data querying, thus reducing human intervention and ensuring compliance. PQ-ECIES was designed by combining elliptic curve cryptography (ECC) and the Kyber algorithm from lattice-based post-quantum cryptography. A dual-key mechanism was employed to generate session keys, where an ECC-derived shared secret was combined with a Kyberderived shared secret through SHA3-256 hashing, followed by key derivation for encryption and authentication. This design provided resilience against Shor's algorithm and other quantum attacks while maintaining efficiency compatible with IoT devices. The blockchain system was implemented using Hyperledger Fabric 1.4.4, with seven organizational nodes and the Raft consensus mechanism. Performance testing included evaluations of data collection accuracy, on-chain latency, query latency, and encryption performance across RSA, advanced encryption standard (AES), and PQ-ECIES.

The IoT-based data collection achieved significantly higher accuracy than manual input, particularly in large-scale sample scenarios such as pesticide residue testing. The average latency for data uploading to the blockchain was 2879 ms, while data query latency averaged 122 ms, both of which met the practical requirements of vegetable supply chain applications. In cryptographic performance testing, PQ-ECIES achieved encryption and decryption of 128 B plaintext in approximately 10-30 ms, outperforming RSA (50-80 ms) and only slightly slower than AES (<10 ms). This result indicates that PQ-ECIES achieved an optimal trade-off between efficiency and security, offering asymmetric encryption benefits such as key distribution and identity verification, along with strong post-quantum resistance. Simulation under quantum attack models confirmed that traditional ECC and AES could be compromised within hours using Shor's and Grover's algorithms, whereas PQ-ECIES maintained resilience due to the lattice-based hardness assumptions of Kyber. From a system-level perspective, three major contributions were identified. First, trustworthiness was enhanced by binding IoT devices to enterprises through Bluetoothbased verification and blockchain's immutable ledger, ensuring data authenticity at the source. Second, privacy protection was achieved by adopting graded visibility: Consumers accessed only public data such as testing results and logistics status, while regulators could decrypt private information (e.g., production location and batch details) via authorized keys, balancing transparency with confidentiality. Third, collaboration across enterprises was improved through the consortium blockchain structure and Fabric channel mechanisms, which eliminated information silos and enabled selective data sharing in real time, reducing inter-organizational access time from weeks to minutes. Experimental validation confirmed that IoT-based collection significantly improved accuracy, blockchain integration achieved acceptable on-chain and query latency, and PQ-ECIES outperformed RSA while offering post-quantum resistance not available in AES.

This study proposed and implemented a vegetable supply chain traceability system that integrates IoT, blockchain, and PQ-ECIES. By deploying nine categories of IoT devices, establishing trusted device-enterprise mappings, and incorporating blockchain's decentralized and tamper-proof ledger, the system ensured reliable data collection and storage. The integration of PQ-ECIES provided dual cryptographic protection, balancing efficiency with long-term quantum security. Beyond technical performance, the system enhanced trust, privacy, and collaboration across the vegetable supply chain, effectively addressing common issues of data forgery, tampering, and cross-enterprise coordination. Overall, the proposed framework demonstrates high potential for real-world deployment in agricultural supply chains, offering a secure, efficient, and future-proof solution to ensure authenticity, reliability, and transparency in vegetable traceability. The study also provides a reference model for extending post-quantum blockchainbased traceability to other agri-food sectors facing similar challenges.