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Secure Semantic Communications: From Perspective of Physical Layer Security

Yongkang Li, Zheng Shi, Han Hu, Yaru Fu, Hong Wang, Hongjiang Lei

TL;DR

This work tackles the risk of semantic information leakage in wireless transmissions by introducing DeepSSC, a DNN-based secure semantic communication system that leverages physical layer security. It employs a two-phase training regime—Phase I to maximize semantic fidelity and Phase II to minimize leakage—with variational inference used to approximate capacity-based objectives and a new S-BLEU metric to quantify semantic security. Results show substantial security gains at high SNR with only a modest decrease in reliability, demonstrating the viability of end-to-end, PLS-enabled secure semantic communications for future 6G-like networks. The framework provides a principled approach combining Transformer-based semantics, neural coding, and information-theoretic security to protect meanings rather than just bits.

Abstract

Semantic communications have been envisioned as a potential technique that goes beyond Shannon paradigm. Unlike modern communications that provide bit-level security, the eaves-dropping of semantic communications poses a significant risk of potentially exposing intention of legitimate user. To address this challenge, a novel deep neural network (DNN) enabled secure semantic communication (DeepSSC) system is developed by capitalizing on physical layer security. To balance the tradeoff between security and reliability, a two-phase training method for DNNs is devised. Particularly, Phase I aims at semantic recovery of legitimate user, while Phase II attempts to minimize the leakage of semantic information to eavesdroppers. The loss functions of DeepSSC in Phases I and II are respectively designed according to Shannon capacity and secure channel capacity, which are approximated with variational inference. Moreover, we define the metric of secure bilingual evaluation understudy (S-BLEU) to assess the security of semantic communications. Finally, simulation results demonstrate that DeepSSC achieves a significant boost to semantic security particularly in high signal-to-noise ratio regime, despite a minor degradation of reliability.

Secure Semantic Communications: From Perspective of Physical Layer Security

TL;DR

This work tackles the risk of semantic information leakage in wireless transmissions by introducing DeepSSC, a DNN-based secure semantic communication system that leverages physical layer security. It employs a two-phase training regime—Phase I to maximize semantic fidelity and Phase II to minimize leakage—with variational inference used to approximate capacity-based objectives and a new S-BLEU metric to quantify semantic security. Results show substantial security gains at high SNR with only a modest decrease in reliability, demonstrating the viability of end-to-end, PLS-enabled secure semantic communications for future 6G-like networks. The framework provides a principled approach combining Transformer-based semantics, neural coding, and information-theoretic security to protect meanings rather than just bits.

Abstract

Semantic communications have been envisioned as a potential technique that goes beyond Shannon paradigm. Unlike modern communications that provide bit-level security, the eaves-dropping of semantic communications poses a significant risk of potentially exposing intention of legitimate user. To address this challenge, a novel deep neural network (DNN) enabled secure semantic communication (DeepSSC) system is developed by capitalizing on physical layer security. To balance the tradeoff between security and reliability, a two-phase training method for DNNs is devised. Particularly, Phase I aims at semantic recovery of legitimate user, while Phase II attempts to minimize the leakage of semantic information to eavesdroppers. The loss functions of DeepSSC in Phases I and II are respectively designed according to Shannon capacity and secure channel capacity, which are approximated with variational inference. Moreover, we define the metric of secure bilingual evaluation understudy (S-BLEU) to assess the security of semantic communications. Finally, simulation results demonstrate that DeepSSC achieves a significant boost to semantic security particularly in high signal-to-noise ratio regime, despite a minor degradation of reliability.
Paper Structure (16 sections, 1 theorem, 13 equations, 5 figures, 1 algorithm)

This paper contains 16 sections, 1 theorem, 13 equations, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Performance Metrics
  3. Secure Semantic Communication System
  4. Wire-Tap Channel Model
  5. System Description
  6. Performance Metrics
  7. BLEU Score
  8. S-BLEU Score
  9. Secure Semantic Communications
  10. Problem Formulation
  11. Phase I of Reliability Assurance
  12. Phase II of Security Guarantee
  13. Model Design
  14. Training Algorithm
  15. Simulation Results
  16. ...and 1 more sections

Key Result

Theorem 1

A variational inference lower bound of $I({\bf{s}};\left. {\bf y}_\kappa\right|h_\kappa)$ is obtained as where $H(\cdot)$ refers to the entropy functional, Step (a) holds by using the non-negative property of KL divergence, Step $(b)$ holds by virtue of the definition of cross entropy, and the cross entropy ${{\cal L}_{{\rm{CE}}}}({\bf{s}},{\bf{\hat{s}}}_\kappa)$ of the semantic decoder ${p(\left

Figures (5)

  • Figure 1: The framework of DeepSSC.
  • Figure 2: The schematic diagram of DeepSSC.
  • Figure 3: BLEU score of 1-gram versus SNR $\gamma_T$.
  • Figure 4: BLEU score of 3-grams versus SNR $\gamma_T$.
  • Figure 5: S-BLEU scores of 1-gram and 3-grams versus SNR $\gamma_T$.

Theorems & Definitions (1)

  • Theorem 1