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Semantic-Enabled 6G Communication: A Task-oriented and Privacy-preserving Perspective

Shuaishuai Guo, Anbang Zhang, Yanhu Wang, Chenyuan Feng, Tony Q. S. Quek

TL;DR

The paper investigates privacy risks in task-oriented semantic communication (ToSC) for 6G and analyzes privacy-preserving strategies for DeepJSCC-based ToSC. It compares feature perturbation approaches (differential privacy and encryption) with intrinsic security methods (adversarial learning and learning-based vector quantization) and discusses explainable learning integration. Experiments on CIFAR-10 and CelebA show that intrinsic-security methods (IBAL and LBVQ) generally offer superior privacy-utility trade-offs across a range of channel conditions, with perturbation methods performing variably depending on the SNR. The work highlights scalability considerations, potential applications (e.g., AR, medical diagnostics, V2X), and future directions including generative AI-based privacy techniques and transfer learning for dynamic ToSC configurations.

Abstract

Task-oriented semantic communication (ToSC) emerges as an innovative approach in the 6G landscape, characterized by the transmission of only vital information that is directly pertinent to a specific task. While ToSC offers an efficient mode of communication, it concurrently raises concerns regarding privacy, as sophisticated adversaries might possess the capability to reconstruct the original data from the transmitted features. This paper provides an in-depth analysis of privacy-preserving strategies specifically designed for ToSC relying on deep neural network-based joint source and channel coding (DeepJSCC). Our study encompasses a detailed comparative assessment of trustworthy feature perturbation methods such as differential privacy (DP) and encryption, alongside intrinsic security incorporation approaches like adversarial learning to train the JSCC and learning-based vector quantization (LBVQ). Our comparative analysis underscores the integration of advanced explainable learning algorithms into communication systems, positing a new benchmark for privacy standards in the forthcoming 6G era.

Semantic-Enabled 6G Communication: A Task-oriented and Privacy-preserving Perspective

TL;DR

The paper investigates privacy risks in task-oriented semantic communication (ToSC) for 6G and analyzes privacy-preserving strategies for DeepJSCC-based ToSC. It compares feature perturbation approaches (differential privacy and encryption) with intrinsic security methods (adversarial learning and learning-based vector quantization) and discusses explainable learning integration. Experiments on CIFAR-10 and CelebA show that intrinsic-security methods (IBAL and LBVQ) generally offer superior privacy-utility trade-offs across a range of channel conditions, with perturbation methods performing variably depending on the SNR. The work highlights scalability considerations, potential applications (e.g., AR, medical diagnostics, V2X), and future directions including generative AI-based privacy techniques and transfer learning for dynamic ToSC configurations.

Abstract

Task-oriented semantic communication (ToSC) emerges as an innovative approach in the 6G landscape, characterized by the transmission of only vital information that is directly pertinent to a specific task. While ToSC offers an efficient mode of communication, it concurrently raises concerns regarding privacy, as sophisticated adversaries might possess the capability to reconstruct the original data from the transmitted features. This paper provides an in-depth analysis of privacy-preserving strategies specifically designed for ToSC relying on deep neural network-based joint source and channel coding (DeepJSCC). Our study encompasses a detailed comparative assessment of trustworthy feature perturbation methods such as differential privacy (DP) and encryption, alongside intrinsic security incorporation approaches like adversarial learning to train the JSCC and learning-based vector quantization (LBVQ). Our comparative analysis underscores the integration of advanced explainable learning algorithms into communication systems, positing a new benchmark for privacy standards in the forthcoming 6G era.
Paper Structure (19 sections, 4 figures, 2 tables)

This paper contains 19 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Shift From Task-Agnostic Communications to ToSC and Privacy Challenges
  3. Privacy Preservation Methods for ToSC
  4. Feature Perturbation Methods
  5. Intrinsic Security Incorporation Strategies
  6. Transceiver Structure Comparison
  7. Cost and Delay Comparison
  8. Experiments and Discussions
  9. Experimental Settings
  10. Information Leakage Under Adversarial Attack
  11. Experimental Results -- Image Classification
  12. Experimental Results -- Face Recognition
  13. Scalability Issue
  14. Potential Applications
  15. Future Research Directions
  16. ...and 4 more sections

Figures (4)

  • Figure 1: Traditional transceivers versus ToSC transceivers.
  • Figure 2: Transceiver structure of four privacy protection methods: differential privacy, encryption, adversarial learning, and vector quantization.
  • Figure 3: Task-accomplishment Performance (i.e., image classification) versus Privacy-preserving Performance (i.e., MI Leakage) on the CIFAR-10 dataset.
  • Figure 4: Visual comparison about all schemes including DeepJSCC-DP-0.9, DeepJSCC-DP-0.1, DeepJSCC-DP-0.05, DeepJSCC-Encryption, IBAL, DeepJSCC-LBVQ (SNR$_{\text{train}}$=SNR$_{\text{test}}$=12dB) for the sample image of the CelebA dataset. The text below each image indicate the strategy and MI Leakage value.