Neural Synchronization with Queries

The neural key exchange algorithm for choosing the relevant inputs is sufficient to achieve a more or less secure key-exchange protocol, however A and B could improve it by taking more information into account, including queries in the training process of the neural networks. Alternatively A and B are generating an input which is correlated with its state and A or B is asking the partner for the corresponding output bit[7]. The overlap between input and weight vector is so low that the additional information does not reveal much about the internal states. But queries introduce a mutual influence between A and B which is not available to an attacking network E. In this work query incorporated to the case of the Hebbian training rule. The probability of a successful attack is calculated for different model parameters using numerical simulations. The results show that queries restore the security against cooperating attackers.

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