Innovative Algorithmic Models for Information Security in Cross-Border Digital Currency Transactions

Abstract

This study explores the information security strategy in digital currency and decentralized international trade from the theoretical and application levels. This strategy can solve the application layer security issues of blockchain technology in cross-border trade. This study proposes a blockchain-based digital currency security improvement framework by combining model analysis with actual needs. The research method includes the improvement of evolutionary game analysis of 51 % double-spending attack, the response strategy of complex double-spending attack and the privacy protection mechanism based on zero-knowledge proof. This fusion gap framework can improve the security of model to the greatest extent. The experimental results numerically show that by reasonably setting the number of transaction confirmations ($M$value), the success rate of double-spending attacks can be significantly reduced. Specifically, when$M=6$, the attack success rate drops to 0.21 %. In addition, the zero-knowledge proof encryption scheme performs well in privacy protection experiments. The accuracy of experimental results has been remained above 99.35 %, and with highest reaching 99.83 %. This result is better than the traditional homomorphic encryption and cipher-text encryption methods.