International Association
for Cryptologic Research

This research extends Abadi and Andersen's exploration of neural networks using secret keys for information protection in multiagent systems. Focusing on enhancing confidentiality properties, we employ end-to-end adversarial training with neural networks Alice, Bob, and Eve. Unlike prior work limited to 64-bit messages, our study spans message sizes from 4 to 1024 bits, varying batch sizes and training steps. An innovative aspect involves training model Bob to approach a minimal error value close to zero and examining its effect on the feasibility of the model. This research unveils the neural networks' adaptability and scalability in encryption and decryption across diverse scenarios, offering valuable insights into their optimization potential for secure communication.