期刊
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
卷 25, 期 3, 页码 2223-2240出版社
SPRINGER
DOI: 10.1007/s10586-021-03425-x
关键词
Blockchain; Supply chain; Counterfeits; Privacy; Logistics
This paper introduces the application of blockchain in the supply chain, proposing a novel method for transaction privacy protection. By generating symmetric keys, employing product codes, and using asymmetric key elliptic curve cryptography, privacy is achieved without compromising traceability and ownership. Experimental results show that the proposed system outperforms existing techniques.
Blockchain was at the top of the 2016 Gartner hype cycle and has been integrated into business profiles by numerous start-ups. Since the emergence of blockchain through Bitcoin, studies have been conducted to increase blockchain applications for nonfinancial uses. A supply chain is a sector where blockchain is anticipated to have crucial applications. In a traditional supply chain, maintaining traceability and ownership remains a serious issue. In the supply chain, blockchain can increase trust, improve traceability, and eliminate the middle man. It makes the supply chain more transparent though, raising the privacy issue. In this paper, a new approach for transaction privacy is proposed by considering ownership and traceability. The proposed system retains the advantages of blockchain and centralised database server. Its novelty lies in achieving privacy by generating symmetric keys, employing product codes and current timestamps, and it uses asymmetric key elliptic curve cryptography for transaction validation and user identification. The proposed system allows product owners to trace the product and enables its transfer. It protects the supply chain from counterfeit products. The Hyperledger Sawtooth blockchain was used for experiments. Security and privacy analysis show that the proposed system can afford privacy without impinging on traceability and ownership. The results estimate that privacy incorporation introduces an overhead of 4.4%. In the experiment, the performance of the proposed system bettered the results of the existing techniques such as POMS and b_verify.
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