Deep Learning Enabled Semantic-Secure Communication with Shuffling

Fupei Chen, Liyao Xiang, Hei Victor Cheng, Kaiming Shen

Abstract: Deep learning and natural language processing draw heavily on the recent progress in semantic communications; this paper examines the security aspect of this cutting-edge technique. Our goal is to improve upon the conventional secure coding methods to strike a superior tradeoff between transmission rate and leakage rate. Toward this end, we devise a novel semantic security communication system wherein the random shuffling pattern serves as the secret key shared. Intuitively, the permutation of words in the same text via shuffling would result in the meaning distortion of the target text to such a great extent that an eavesdropper can no longer recover the semantic truth. The proposed method can be rephrased as maximizing the transmission rate while minimizing the semantic error probability under the given leakage rate constraint. Simulations demonstrate the significant advantage of the proposed method over the benchmark in boosting secure transmission, especially when channels are prone to strong noise and unpredictable fading, can achieve up to 60% performance gain.