Blockchain-based covert data transmission by key relationship in Web3.0 environments
Yin Lei et al.
What the paper says
Purpose This study aims to propose blockchain-based covert data transmission schemes for modern decentralized applications, addressing the challenges of secure covert communication in resource-constrained environments. The research seeks to enhance the covertness and efficiency of communications in Web3.0 environments, where transparency and decentralized architectures dominate. Design/methodology/approach Two blockchain-based lightweight covert data transmission schemes are proposed. The first scheme involves sharing private keys, while the second scheme avoids sharing private keys to prevent the potential loss of digital currency. Both schemes are designed to be implemented on lightweight devices. The solutions were tested on Raspberry Pi and the Ethereum Testnet to assess their feasibility. Findings The results demonstrate that both schemes improve covertness and efficiency for Web3.0-compatible devices. The second scheme, which avoids sharing private keys, effectively addresses the risk of blockchain private key leakage while maintaining low resource consumption. These findings provide evidence that the proposed solutions are suitable for lightweight devices and offer enhanced security without compromising performance. Originality/value This research offers a novel approach to covert data transmission in Web3.0 environments by leveraging blockchain technology. It provides valuable insights into secure communication methods, contributing to the development of efficient and secure data transmission mechanisms for decentralized applications. The study’s findings highlight areas for future research and practical applications in blockchain-based Web3.0 security.
1 citation
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.16 × 0.4 = 0.06 |
| M · momentum | 0.53 × 0.15 = 0.08 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.