Prediction of Financial Data Security Risks and Privacy Protection Methods for Listed Companies Based on Hypergraph Learning

With the acceleration of digitalization, financial data of listed companies are facing complex and diverse security risks and privacy leakage hazards. To improve the accuracy and effectiveness of data security management, this study proposes a financial data security risk prediction and privacy protection method for listed companies based on hypergraph learning. It is based on hypergraph learning to construct a risk prediction model, which optimizes the hyperedge weight algorithm to explore the complex relationships between multi-dimensional financial data, and integrates bidirectional gated loop units to achieve dynamic risk prediction. A layered privacy protection strategy based on differential privacy has been designed, which adaptively allocates privacy budget according to data sensitivity. The experiment showed that this risk prediction method had an accuracy rate of 93.1% on financial datasets, which was 9.2% higher than traditional graph attention networks. The false positive rate was only 2.8%, and it could accurately identify 8 potential security risks. When the privacy budget was 0.6, the privacy protection method controlled the data utility loss at 11.5%, which improved the information utilization rate by 25.3% compared to traditional generalized anonymity methods. It also met the privacy compliance standards of the financial industry in six scenarios, including data transactions and internal audits. Through the coordinated design of risk prediction and privacy protection, this study establishes a comprehensive financial data security management framework for listed companies. The method’s core innovation lies in systematically resolving the balance between security safeguards and data utility. By integrating hypergraph learning with BiGRU prediction models, it achieves high-precision dynamic risk alerts for complex scenarios while preserving data analytical value. Additionally, the adaptive hierarchical protection strategy based on differential privacy provides compliant and measurable security for sensitive information. This system delivers a practical, integrated technical solution for the financial industry that balances precise risk alerts with privacy controllability.