A Deep-Learning Approach for ECG-Based Cryptographic Key Generation

This poster was presented at the 2025 Silicon Valley Cybersecurity Conference (SVCC). The project develops neural architectures for producing person-specific 256-bit cryptographic keys from ECG segments. Evaluation focuses on consistency across recordings, separation between individuals, and randomness under the NIST Statistical Test Suite.

The published architecture combines convolutional feature extraction with multihead attention.