2006/12/31 by Andreas Ruttor, Wolfgang Kinzel, Ido Kanter
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Chaos-based Image/Signal Encryption #Fractal and DNA sequence analysis #Neural Networks and Applications #cond-mat.dis-nn
paper · pdf · doi:10.1103/physreve.75.056104
published as Phys. Rev. E 75, 056104 (2007) · 9 pages, 15 figures; typos corrected
openalex publication_date 2007/05/09 · arxiv created 2007/11/01 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.