Author
Listed:
- Sheng Peng
(Academy of Management, Guangdong University of Science and Technology, Dongguan 523083, China
Zhuhai Yingying Technology Co., Ltd., Zhuhai 519080, China)
- Linkai Zhu
(Information Technology School, Hebei University of Economics and Business, Shijiazhuang 050061, China)
- Shanwen Hu
(Faculty of Data Science, City University of Macau, Macau 999078, China)
- Zhiming Cai
(Faculty of Digital Science and Technology, Macau Millennium College, Macau, China)
- Wenjian Liu
(Faculty of Data Science, City University of Macau, Macau 999078, China)
Abstract
Blockchain technology, initially developed as a decentralized and transparent mechanism for recording transactions, faces significant privacy challenges due to its inherent transparency, exposing sensitive transaction data to all network participants. This study proposes a blockchain privacy protection algorithm that employs a digital mutual trust mechanism integrated with advanced cryptographic techniques to enhance privacy and security in blockchain transactions. The contribution includes the development of a new dynamic Byzantine consensus algorithm within the Practical Byzantine Fault Tolerance framework, incorporating an authorization mechanism from the reputation model and a proof consensus algorithm for robust digital mutual trust. Additionally, the refinement of homomorphic cryptography using the approximate greatest common divisor technique optimizes the encryption process to support complex operations securely. The integration of a smart contract system facilitates automatic and private transaction execution across the blockchain network. Experimental evidence demonstrates the superior performance of the algorithm in handling privacy requests and transaction receipts with reduced delays and increased accuracy, marking a significant improvement over existing methods.
Suggested Citation
Sheng Peng & Linkai Zhu & Shanwen Hu & Zhiming Cai & Wenjian Liu, 2024.
"Enhancing Global Blockchain Privacy via a Digital Mutual Trust Mechanism,"
Mathematics, MDPI, vol. 12(10), pages 1-20, May.
Handle:
RePEc:gam:jmathe:v:12:y:2024:i:10:p:1481-:d:1391771
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