Matching Optimization for Automated Negotiation: From a Privacy-Enhanced Data Modeling Perspective

Automated negotiation in multi-agent electronic commerce environments relies heavily on efficient and reliable matching mechanisms to connect negotiation participants. However, existing matching protocols often fail to ensure transaction security and user data privacy, while also lacking adaptability to dynamic negotiation contexts. To address these challenges, this study proposes a privacy-enhanced multi-agent matching optimization framework that integrates trust evaluation, privacy protection, and adaptive decision-making. First, a trust-based negotiation relationship network is constructed through complex network analysis to establish a secure and trustworthy negotiation environment. Second, a privacy-enhanced automated negotiation protocol is developed, employing the cumulative distribution function to transform sensitive data into probabilistic representations, thereby safeguarding user privacy without compromising data availability. Finally, a reinforcement learning algorithm is incorporated to optimize the matching process dynamically, using satisfaction as the reward function to achieve efficient and Pareto-optimal results. A series of experiments verify the framework’s effectiveness, demonstrating significant improvements in system robustness, adaptability, and matching accuracy. This study aims to provide a comprehensive solution that integrates trust network modeling, privacy protection, and adaptive matching optimization, serving as a valuable reference for the development of secure and intelligent automated negotiation platforms.