Synchronization of Neural Networks by Mutual Learning and Its Application to Cryptography
Abstract
Two neural networks that are trained on their mutual output synchronize to an identical time dependant weight vector. This novel phenomenon can be used for creation of a secure cryptographic secret-key using a public channel. Several models for this cryptographic system have been suggested, and have been tested for their security under different sophis- ticated attack strategies. The most promising models are networks that involve chaos synchronization. The synchronization process of mutual learning is described analytically using statistical physics methods.
Cite
Text
Klein et al. "Synchronization of Neural Networks by Mutual Learning and Its Application to Cryptography." Neural Information Processing Systems, 2004.Markdown
[Klein et al. "Synchronization of Neural Networks by Mutual Learning and Its Application to Cryptography." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/klein2004neurips-synchronization/)BibTeX
@inproceedings{klein2004neurips-synchronization,
title = {{Synchronization of Neural Networks by Mutual Learning and Its Application to Cryptography}},
author = {Klein, Einat and Mislovaty, Rachel and Kanter, Ido and Ruttor, Andreas and Kinzel, Wolfgang},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {689-696},
url = {https://mlanthology.org/neurips/2004/klein2004neurips-synchronization/}
}