I propose SNN-Comprypto, a novel cryptographic system that leverages the chaotic dynamics of Spiking Neural Networks (SNNs) to perform simultaneous data compression and encryption. Unlike conventional methods that treat compression and encryption as separate processes, this approach integrates both within a single reservoir computing architecture. **Version History:** - **v1**: Core system with predictive compression and chaotic encryption. Passes all 9 NIST SP 800-22 randomness tests. Achieves 100% lossless reconstruction and strong avalanche effect (0.70% match rate with 1-bit key change). - **v2**: Introduces temperature parameter as a second cryptographic key (0.0001 difference causes complete decryption failure). Presents phase transition analysis identifying optimal neuron counts (critical point at 100 neurons, sweet spot at 240 neurons). - **v3**: Adversarial evaluation framework demonstrating SNN superiority for random number generation. Results from 100,000+ rounds show SNN achieves 0.39% prediction rate (matching theoretical random), while DNN is 18× and LSTM is 55× more predictable. - **v4 (NEW)**: Theoretical chaos analysis with Lyapunov exponents and entropy measurements. SNN achieves near-perfect entropy (7.998/8.0 bits) with positive Lyapunov exponents, confirming true chaotic dynamics. Adversarial attack resistance evaluation (4/5 attack types resisted). IV/nonce recommendation for secure deployment added. Source code: https://github.com/hafufu-stack/temporal-coding-simulation
Hiroto Funasaki (Sun,) studied this question.