A Secure Multimodal Biometric Data Protection Framework Using Optimized CNN, GAN-Based Privacy Preservation, and ElGamal Cryptography

We propose a secure biometric data protection (SBDP) system, which uses artificial intelligence (AI) and encryption methods to prevent forgery and keep the biometric data private and intact. The proposed SBDP approach integrates deep learning-based feature extraction with robust encryption and authentication mechanisms in a single pipeline. We use the optimized convolutional neural network (OCNN) to obtain unique features from multimodal biometric inputs like fingerprints, facial photos, and retinal scans. This works well because it learns how to represent data efficiently. To reduce the risks of raw biometric exposure, we adopt a generative adversarial network (GAN) to generate synthetic biometric representations that maintain essential characteristics while reducing sensitivity to data leakage. The biometric features and images are encrypted using the ElGamal cryptosystem to provide security assurance, while the digital signature scheme based on the SHA-256 hash function is used to provide data integrity and authenticity. Experimental results show good performance of all components of the framework. The optimized CNN obtains a classification accuracy of more than 99.8%, while the GAN shows stable training behavior with the discriminator and generator losses converging to around 0.3 and 4.0, respectively. The cryptographic module guarantees encryption dependability and signature verification efficacy across all evaluated scenarios. The integrated system provides effective protection of biometric data from unauthorized access, tampering and identity forgery. The SBDP framework is a promising solution for defense, healthcare and digital identity management, ensuring secure transmission and storage of biometric data.