Shuffling for Semantic Secrecy

TL;DR

This paper introduces a novel semantic security communication system using random shuffling of feature sequences to enhance security in semantic communications, especially under noisy and fading channels.

Contribution

It proposes a new shuffling-based method that improves secure transmission rates and reduces leakage, adaptable as a plugin to existing systems.

Findings

Significant boost in secure transmission performance.

Effective in noisy and fading channel conditions.

Flexible integration with existing semantic communication systems.

Abstract

Deep learning draws heavily on the latest progress in semantic communications. The present paper aims to examine the security aspect of this cutting-edge technique from a novel shuffling perspective. Our goal is to improve upon the conventional secure coding scheme to strike a desirable tradeoff between transmission rate and leakage rate. To be more specific, for a wiretap channel, we seek to maximize the transmission rate while minimizing the semantic error probability under the given leakage rate constraint. Toward this end, we devise a novel semantic security communication system wherein the random shuffling pattern plays the role of the shared secret key. Intuitively, the permutation of feature sequences via shuffling would distort the semantic essence of the target data to a sufficient extent so that eavesdroppers cannot access it anymore. The proposed random shuffling method also exhibits its flexibility in working for the existing semantic communication system as a plugin. 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.