Generative blockchain-based covert communication model based on Markov chain

Abstract

To solve the problems of high channel construction risk, information crossover, and insufficient concealment in the blockchain covert communication, a generative blockchain-based covert communication model based on Markov chain was proposed.First, the text data set was used by sender to obtain the candidate words set and trained the Markov model to obtain the transition probability matrix, generated the Huffman tree set.Secret message to be transmitted was performed iterative Huffman decoding on the binary stream to obtain a set of highly readable carring-secret message statements that conformed to normal language and semantic characteristics, a generative steganography was used to complete secret message embedding.Then, the carring-secret message was ring-signed and published to the blockchain as a normal transaction packing and block generation were completed in the network.Finally, the same text data set was used by the receiver to obtain the Huffman tree of transition probability weights, the binary stream of secret message was obtained by reverse operation.Simulation results demonstrate that, compares with the current similar models, the proposed model can further improve the embedding strength and time efficiency, reduce the risk of covert channel construction, avoid information crossover, and improve the concealment.

Keywords

• covert communication;blockchain;Markov chain;generative text steganography;ring signature