Zhigang Gao

The blockchain-based Automated Market Maker (AMM) mechanism establishes a multilateral trading market for multi-source homogeneous data assets. Its advantage lies in realizing algorithmic dynamic pricing and automated circulation through decentralized liquidity pools, effectively avoiding the single-point failure issues and pricing inefficiencies associated with traditional centralized platforms, while significantly improving the trading efficiency and value conversion potential of data assets. However, in high-frequency, large-scale, multilateral data trading scenarios, these AMM liquidity pools face intensified Impermanent Loss (IL) that cannot be easily addressed by conventional risk mitigation approaches, necessitating domain-specific tailored solutions. To address this issue, our study proposes a blockchain on-chain liquidity pool-based data trading market model. Through mathematical modeling and simulation experiments, we quantify how trader behavioral characteristics, including price sensitivity differentials, heterogeneous trading frequencies, and trading size variations, impact the value of AMM liquidity pool. On this basis, we propose a Behavior-Aware Real-time Trading-driven Impermanent Loss optimization method (BART-IL), which uses multi-factor scoring to dynamically sequence trades, generating low-impermanent-loss execution paths to mitigate risks for Liquidity Providers (LPs). Experimental results demonstrate that BART-IL reduces IL for LPs, capping maximum loss at 25.6% in large-scale trading scenarios and achieving over 40% loss reduction in high-frequency-dominant markets. Accordingly, the method substantially lowers the overall risk of data trading. This research addresses the adaptability bottleneck of AMM mechanisms for non-standard assets. By integrating innovations in mechanism design and algorithm optimization, we construct a low-cost blockchain-based decentralized data trading framework with enhanced fairness, offering important implications for ensuring the robustness and attractiveness of data trading platforms.

With the widespread adoption of cryptocurrencies, the ability to conduct continuous offline payments has increasingly become a critical technological requirement. In network-constrained scenarios, current dual-offline payment technologies are useful for single transactions. However, their limitations in continuous payment scenarios have become increasingly evident, making them unable to meet real-world application needs. This has prompted the industry to demand more urgent innovations in research on continuous offline payment capabilities. To address these challenges, this paper proposes a continuous dual-offline payment system capable of supporting multiple continuous payments. The system integrates elliptic curve cryptography (ECC) and zero-knowledge proof (ZKP) technology to generate secure asset credentials, ensuring both immutability and privacy credentials throughout the offline payment lifecycle. A dynamic credential decomposition mechanism enables the splitting of input credentials into change credentials and receipt credentials, facilitating uninterrupted dual-offline payments between hardware wallets. Additionally, it incorporates a batch verification scheme based on smart contracts, utilizing zero-balance verification and chained hash tracing to ensure payment uniqueness and prevent double-spending attacks, thereby guaranteeing the verifiability and validity of payment settlements. Experimental evaluations demonstrate that the proposed system reduces gas consumption per payment and improves execution efficiency during batch processing, combining high security with strong performance. This research provides a feasible solution for the application of digital currencies in offline scenarios, carrying significant theoretical value and practical significance for driving technological innovation and application expansion in the cryptocurrency field. In addition to cryptocurrency payments, the proposed system is also applicable to IoT and sensor network environments. Many IoT devices operate in disconnected or network-limited areas and require secure micro-transactions. Our dual-offline payment mechanism supports such scenarios, as the main cryptographic operations are lightweight enough for typical IoT hardware. This further extends the practical value of our system beyond traditional cryptocurrency payments.

Current research on cryptocurrency dual-offline payment systems has garnered significant attention from both academia and industry, owing to its potential payment feasibility and application scalability in extreme environments and network-constrained scenarios. However, existing dual-offline payment schemes exhibit technical limitations in privacy preservation, failing to adequately safeguard sensitive data such as payment amounts and participant identities. To address this, this paper proposes a privacy-preserving dual-offline payment method utilizing a cryptographic challenge-response mechanism. The method employs zero-knowledge proof technology to cryptographically protect sensitive information, such as the payer’s wallet balance, during identity verification and payment authorization. This provides a technical solution that balances verification reliability with privacy protection in dual-offline transactions. The method adopts the payment credential generation and credential verification mechanism, combined with elliptic curve cryptography (ECC), to construct the verification protocol. These components enable dual-offline functionality while concealing sensitive information, including counterparty identities and wallet balances. Theoretical analysis and experimental verification on 100 simulated transactions show that this method achieves an average payment generation latency of 29.13 ms and verification latency of 25.09 ms, significantly outperforming existing technology in privacy protection, computational efficiency, and security robustness. The research provides an innovative technical solution for cryptocurrency dual-offline payment, advancing both theoretical foundations and practical applications in the field.