Dynamics of neural cryptography

Journal Article · Tue May 15 00:00:00 EDT 2007 · Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics

DOI:https://doi.org/10.1103/PHYSREVE.75.056104· OSTI ID:21072437

Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido ^[1]

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1. Institut fuer Theoretische Physik, Universitaet Wuerzburg, Am Hubland, 97074 Wuerzburg (Germany)

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.

• OSTI ID: 21072437
• Journal Information: Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, Vol. 75, Issue 5; Other Information: DOI: 10.1103/PhysRevE.75.056104; (c) 2007 The American Physical Society; Country of input: International Atomic Energy Agency (IAEA); ISSN 1063-651X
• Country of Publication: United States
• Language: English

Related Subjects

71 CLASSICAL AND QUANTUM MECHANICS
GENERAL PHYSICS
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY
COMPUTERIZED SIMULATION
DISTRIBUTION
ENTROPY
FLUCTUATIONS
GRAPH THEORY
LEARNING
NEURAL NETWORKS
PARITY
QUANTUM CRYPTOGRAPHY
RANDOMNESS
SCALING LAWS
SECRECY PROTECTION
STOCHASTIC PROCESSES
SYNCHRONIZATION