Synchronization of neural networks by mutual learning and its application to cryptography

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

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Einat Klein, Rachel Mislovaty, Ido Kanter, Andreas Ruttor, Wolfgang Kinzel


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.