A Survey of Secure Semantic Communications

TL;DR

This survey reviews security and privacy challenges in semantic communication systems, detailing threats across the entire lifecycle and summarizing mitigation techniques like cryptography, blockchain, and robust learning.

Contribution

It provides a comprehensive overview of security issues and solutions specific to semantic communication, a key technology for 6G networks, covering all lifecycle stages.

Findings

Identifies security and privacy threats during model training, transfer, and transmission phases.

Summarizes techniques such as differential privacy, cryptography, and blockchain for threat mitigation.

Outlines future research directions in secure semantic communication.

Abstract

Semantic communication (SemCom) is regarded as a promising and revolutionary technology in 6G, aiming to transcend the constraints of ``Shannon's trap" by filtering out redundant information and extracting the core of effective data. Compared to traditional communication paradigms, SemCom offers several notable advantages, such as reducing the burden on data transmission, enhancing network management efficiency, and optimizing resource allocation. Numerous researchers have extensively explored SemCom from various perspectives, including network architecture, theoretical analysis, potential technologies, and future applications. However, as SemCom continues to evolve, a multitude of security and privacy concerns have arisen, posing threats to the confidentiality, integrity, and availability of SemCom systems. This paper presents a comprehensive survey of the technologies that can be utilized to secure SemCom. Firstly, we elaborate on the entire life cycle of SemCom, which includes the model training, model transfer, and semantic information transmission phases. Then, we identify the security and privacy issues that emerge during these three stages. Furthermore, we summarize the techniques available to mitigate these security and privacy threats, including data cleaning, robust learning, defensive strategies against backdoor attacks, adversarial training, differential privacy, cryptography, blockchain technology, model compression, and physical-layer security. Lastly, this paper outlines future research directions to guide researchers in related fields.