ArticleScientific reports2025
Blockchain consensus algorithm for supply chain information security sharing based on convolutional neural networks.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- A privacy-preserving distributed protocol for secure data sharing in untrusted supply chain.Scientific reports · 2026Article
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Authors and funding
3 authors.
Funding
Abstract
To solve the problems of data silos and information asymmetry in traditional supply chain information security sharing, this article combines Convolutional Neural Networks (CNN) and blockchain consensus algorithms, analyzes data and uses blockchain for secure sharing, so that all parties can obtain and verify data in real time, improve the overall operational efficiency of the supply chain, and promote information transparency and sharing efficiency. CNN can be used to analyze data in the supply chain. Training on real digital images ensures data privacy and improves the accuracy and efficiency of data processing. Blockchain technology can be introduced into supply chain information sharing to ensure the immutability and transparency of data. This article introduces a federated learning (FL) mechanism to improve consensus algorithms, which improves the efficiency of model training. Among them, each link in the FL process is rigorously verified and recorded through the consensus mechanism of blockchain, ensuring the security and reliability of the entire process. This article adopts an improved consensus algorithm, PoDaS (Proof of Data Sharing), whose core idea is to use the computational consumption generated during FL as proof of workload. The specific steps include: local model training and uploading, model update verification shield, and model update aggregation. The PoDaS algorithm combines the advantages of PoW (Proof of Work) and PoS (Proof of Stack) to ensure the fairness of the consensus mechanism and reduce the waste of computing resources. By comparing and analyzing the block time and model accuracy of three algorithms, the superiority of PoDaS algorithm in block time and model accuracy was verified. The experimental results show that the PoDaS algorithm is significantly better than the PoW algorithm in terms of block generation time, and slightly better than the PoS algorithm. In terms of model accuracy, the PoDaS algorithm is significantly superior to traditional PoW and PoS algorithms. Its model accuracy reaches 96.00%, reflecting the effectiveness and practicality of the PoDaS consensus algorithm in the sharing of supply chain information security.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.