A federated supply chain finance risk control method based on personalized differential privacy

With the rapid development of supply chain finance, effectively managing risks while safeguarding participant data privacy has become a critical area of research. However, existing traditional risk control models predominantly rely on centralized data processing, which leads to the phenomenon of ”data silos,” hindering the flow and sharing of information. Furthermore, the significant privacy risks associated with centralized processing restrict collaboration among financial institutions, exacerbating the challenges of risk management. In this context, this study proposes a federated risk control method for supply chain finance based on personalized differential privacy optimization. This approach introduces a personalized differential privacy mechanism, enabling different institutions to collaboratively optimize model parameters without directly exchanging sensitive data. This methodology not only effectively safeguards data privacy but also enhances the overall performance of risk control, facilitating multi-party collaboration. Experimental results indicate that, compared to traditional centralized risk control models and other privacy protection methods, the proposed solution demonstrates favorable outcomes in terms of predictive accuracy and model performance while adhering to data privacy protection requirements. This research lays a theoretical foundation for the future development of safer and more efficient cross-institutional risk control systems and provides new insights and technical support for innovative risk management in the field of supply chain finance.