Dynamics of neural cryptography
Andreas Ruttor, Wolfgang Kinzel, Ido Kanter

TL;DR
This paper models the synchronization dynamics of neural networks used in cryptography, showing that bidirectional synchronization is faster and more reliable than unidirectional learning, which enhances security in neural key-exchange protocols.
Contribution
It provides an analytical random walk model for neural synchronization, compares bidirectional and unidirectional dynamics, and evaluates the security implications for neural cryptography.
Findings
Bidirectional synchronization leads to full network synchronization.
Learning relies on fluctuations and is slower than synchronization.
The key space grows exponentially, making brute-force attacks infeasible.
Abstract
Synchronization of neural networks has been used for novel 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…
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