Distributed Hybrid Quantum Computing Applications into Battery Cell Manufacturing Industries as per the Industries 5.0 - Biswaranjan Senapati; Bharat S Rawal (Cloud Computing and Data Science, 2025)
In distributed computing, data trading mechanisms are essential for ensuring the sharing of data across multiple computing nodes. Nevertheless, they currently encounter considerable obstacles, including low accuracy in matching trading parties, ensuring fairness in transactions, and safeguarding data privacy throughout the trading process. To address these issues, we put forward a data trading security scheme based on zero-knowledge proofs and smart contracts. In the phase of preparing the security parameters, the objective is to reduce the complexity of generating non-interactive zero-knowledge proofs and to enhance the efficiency of data trading. In the pre-trading phase, we propose attribute atomic matching smart contracts based on precise data property alignment. The goal is to get trading parties to match data attributes in a very specific way. During the trading execution phase, we use lightweight cryptographic algorithms based on Elliptic Curve Cryptography (ECC) and non-interactive zero-knowledge proofs to encrypt trading data twice and create attribute proof contracts. This keeps the data safe and private. The results of experiments conducted on the Ethereum platform in an industrial Internet of Things (IoT) scenario demonstrate that our scheme maintains stable and low-cost consumption while ensuring accuracy in matching and privacy protection. Especially in battery industrial manufacturing, the application of distributed computing is in huge demand and essential to maintaining healthier technology integration among various systems and technological nodes to better manage energy cells within the battery management system.