Fingerprint-Based Cryptographic Key Generation: A Deep Dive into CNN Training, Hyperdimensional Encoding and Neuro-Symbolic Error Correction

Foreword

Modern cybersecurity challenges increasingly demand solutions that blend advanced machine learning with robust cryptographic techniques. One emerging area at this intersection is fingerprint-based key generation, where users’ biometric data is transformed into secure cryptographic keys. In this article, we will:

1. Train a Convolutional Neural Network (CNN) on the SOCOFing fingerprint dataset.
2. Extract features from the trained CNN to form a compact representation of each fingerprint.
3. Map these features into a high-dimensional binary hypervector using random projections.
4. Apply neuro-symbolic error correction to enhance stability and reliability.
5. Generate a final cryptographic key using SHA-256 hashing.

This comprehensive walkthrough covers the mathematical foundations, includes detailed code snippets, showcases real-world use cases, and highlights the benefits of this approach. By the end, you’ll have a clear understanding of how to leverage deep learning and hyperdimensional computing for secure biometric key generation.