Implicit Authentication in Neural Key Exchange Based on the Randomization of the Public Blockchain

Abstract

A neural key exchange is a secret key exchange technique based on neural synchronization of the neural network. Since the neural key exchange is based on synchronizing weights within the neural network structure, the security of the algorithm does not depend on the attacker’s computational capabilities. However, due to the neural key exchange’s repetitive mutual-learning processes, using explicit user authentication methods –such as a public key certificate– is inefficient due to high communication overhead. Implicit authentication based on information that only authorized users know can significantly reduce overhead in communications. However, there was a lack of realistic methods to distribute secret information for authentication among authorized users. In this paper, we propose the concept idea of distributing shared secret values for implicit authentication based on the randomness of the public blockchain. Moreover, we present a method to prevent the unintentional disclosure of shared secret values to third parties in the network due to the transparency of the blockchain.