Private Inputs to Tree Parity Machine

Neural cryptography is based on synchronization of tree parity machines by mutual learning. We extend previous key-exchange protocols by replacing random inputs with queries depending on the current state of the neural networks. The results show that queries restore the security against attackers. We further restrict amount information available to an attacker by keeping seed of pseudo randomnumber generator private.