High-efficiency storage and retrieval model to trace agri-food supply chain using redactable blockchain

A blockchain-based agri-food traceability can be expected to remedy the inherent trust within agri-food supply chains. However, the immutable and redundant nature of traditional blockchain storage has posed formidable challenges, particularly on the scalability of data storage for blockchain nodes. Agri-food traceability has also been limited to historically impede the widespread adoption in recent years. In this study, an efficient storage and query model was introduced to specifically design for the agri-food supply chain traceability, in order to leverage the concept of a redactable blockchain. The cyclical nature of agri-food traceability data was examined to form the model. The lifecycle of the supply chain was analyzed for the agri-food products. The opportunities were identified for streamline data management. A key innovation involved the strategic offloading of traceability data over the lifecycle. Storage resources were optimized to preserve the essential traceability functionalities. The information remained accessible throughout the supply chain journey. Furthermore, the counter Bloom filter was incorporated to enhance the operational efficiency of data offloading. The high false positive rates were reduced for the data manipulation in redactable blockchains. False positives were effectively reduced to significantly enhance the overall query efficiency of the agri-food traceability system, thereby facilitating expedient access to accurate supply chain information. A counter Bloom filter was operated to leverage the probabilistic hashing techniques. The large datasets were efficiently managed and queried to minimize the occurrence of false positives. A robust mechanism was translated to verify the accuracy of traceability information post-data offloading in the context of the agri-food traceability system. A compact representation of recently offloaded data was maintained to employ the efficient hash functions. The counter Bloom filter effectively reduced the likelihood of mistakenly, in order to identify non-existent data during queries. The efficacy of the improved model was underscored to validate the comprehensive empirical data. The extensive experiment was conducted over a simulated 60-month operational period. Notably, the better performance of the model was achieved with a remarkable 48.70% reduction in storage volume, compared with the conventional agri-food blockchain traceability systems. This reduction was attributed to the strategic lifecycle-based data management, and the storage was optimized without compromising data integrity. The improved model was often verified in a simulated scenario involving 1 000 block records and a 30% data offloading rate. There was a notable 21 percentage points decrease in the false positive rates, indicating the efficacy of the integrated counter Bloom filter with the data accuracy post-offloading. Moreover, there was a commendable 19.02% enhancement in the data query efficiency, compared with the traditional approaches. The compelling solution fully met the operational demands of large-scale agri-food supply chain environments. The blockchain-based agri-food traceability was presented to facilitate the widespread deployment. A significant advancement was achieved in data integrity with the pragmatic storage and query optimizations in the field. Beyond technical innovation, a robust framework was offered to enhance transparency, accountability, and consumer confidence across agri-food supply chains. Looking ahead, the scalability and adaptability of the improved model can promise to support the diverse applications within the agri-food sector. Product authenticity and quality assurance can be enhanced to enable efficient recalls and regulatory compliance. As the blockchain continues to evolve, insights can be gained to pave the way for future advancements in agri-food traceability and industry-wide transformation toward a more resilient and sustainable supply chain. © 2024 Chinese Society of Agricultural Engineering. All rights reserved.