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

TL;DR

This paper explores privacy-preserving strategies for task-oriented semantic communication in 6G, focusing on deep neural network-based joint source-channel coding, and compares methods like differential privacy, encryption, and adversarial learning.

Contribution

It provides a comprehensive analysis and comparison of privacy-preserving techniques tailored for ToSC in 6G, establishing new benchmarks for privacy standards.

Findings

Differential privacy and encryption effectively protect data privacy.

Adversarial learning enhances intrinsic security in JSCC.

The study sets new benchmarks for privacy in 6G semantic communication.

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.