Design and Simulation of a Federated Learning-Based Multilateral Data Privacy Protection and Ethical Collaborative Governance Model for Cross-Border E-Commerce
Abstract
A learning-driven multilateral system that combines technical confidentiality safeguards with regulatory ethics compliance requirements. By integrating an adaptive heterogeneous federation architecture, blockchain-based protocols, and strategic collaboration incentives, the framework creates a secure and verifiable environment for ethical data exchange across different legal domains. Core breakthroughs include a context-sensitive Differential Privacy (DP) and Partial Homomorphic Encryption (PHE) three-layer Federated Learning (FL) architecture that achieves 97.3% data protection integrity while maintaining 89.4% customs risk prediction accuracy in processing OECD cross-border transaction records (2023 - 2025). An automated governance engine translates the EU's General Data Protection Regulation (GDPR), China's Personal Information Protection Law (PIPL), and ASEAN's data sovereignty regulations into enforceable algorithmic constraints, resolving 83.6% of simulated ethical conflicts (e.g., the China-EU user profiling controversy). A contribution quantification mechanism reduced free-riding behavior by 67.2% compared to traditional FL methods. Experimental validation using Alibaba's global trade data (12.8 million cross-border transactions) shows that: privacy leakage is reduced from 15.1% (centralized machine learning) to 1.9% (p<0.001); ethics review latency stabilizes at 162 ms under 10,000TPS load; and cross-jurisdictional coordination efficiency improves compared to…
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