Efficient and Atomic Cross-blockchain Transaction Processing for Decentralized Web3 Applications
Web3 has received considerable attention from industry and academia due to its groundbreaking revolution to decentralize the current centralized web. As a fundamental building block, blockchain provides web3 with the ability to rebuild trust in the trustless decentralized web. More and more blockchain-based web3 applications are taking place and becoming successful. Many web3 applications generate coupled data and transactions from multiple blockchains and demand efficient and atomic cross-blockchain transaction processing; however, existing approaches rarely consider transaction atomicity, and the processing efficiency is inferior. In this work, we formally model multi-blockchain systems and formulate the cross-blockchain transaction processing problem. Then, we propose a novel consensus mechanism, namely proof of credible contributions, to solve the efficiency and atomicity issues in cross-blockchain transaction processing. Inside, we define credible contributions as a measure of blockchain nodes’ credits and workload, so as to make nodes compete to handle as many cross-blockchain transactions as possible. Furthermore, we propose the flag-referenceconfirm protocol to guarantee transaction atomicity. Extensive experimental results indicate the efficiency and effectiveness of proof of credible contributions.
Authors
- Yuqin Wang (ORCID: https://orcid.org/0000-0002-3063-3066)
- Hongbo Liu (ORCID: https://orcid.org/0000-0001-9296-9975)
- Shan Jiang (ORCID: https://orcid.org/0000-0002-4727-4856)
- Shan Wang (ORCID: https://orcid.org/0000-0002-6990-8071)
- Jiannong Cao (ORCID: https://orcid.org/0000-0002-2725-2529)
- Zhixuan Liang (ORCID: https://orcid.org/0000-0002-4851-1278)
Institutions
- Hong Kong Polytechnic University (HK)
Publication Details
- Published
- 2024-12-02
- DOI
- https://doi.org/10.1109/swc62898.2024.00056
- Citations
- 1
- Primary Topic
- Blockchain Technology Applications and Security
- Type
- article
- Field-Weighted Citation Impact
- 0.78
Funders
- Impact Fund
- Hong Kong Polytechnic University