Convolutional Neural Network-Integrated Biometric Feature Encryption for Financial Internet of Things.

With the rapid development of financial Internet of Things (FIoT) scenarios, protecting the security and privacy of personal biometric data has become particularly important. The study provides a detailed introduction to the proposed algorithm framework, which includes key steps such as the preprocessing of biometric features, feature extraction, feature fusion and encryption. We utilize the powerful capabilities of the convolutional neural network (CNN) in the feature extraction stage to identify and extract key information from biometric features, which are then used to generate encryption keys. Through this method, the research not only improves the accuracy of feature extraction but also enhances the complexity and security of the key. The research also enhanced the algorithm's ability to resist attacks by integrating multiple cryptographic techniques, such as advanced encryption standards and elliptic curve cryptography (ECC), to defend against potential security threats. The experimental results show that our algorithm significantly improves the security and privacy protection capabilities of the system while maintaining high recognition accuracy. In addition, we also explored future research directions, including exploring more types of biometric features, further optimizing algorithm parameters, and enhancing the algorithm's resistance to attacks. The biometric encryption algorithm integrating CNN proposed in this paper provides a new security solution for financial IoT scenarios. [ABSTRACT FROM AUTHOR]