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.

翻译：语义通信（SemCom）被视为6G中一项具有前景的革命性技术，旨在通过滤除冗余信息并提取有效数据的核心，从而超越"香农陷阱"的限制。与传统通信范式相比，语义通信具有若干显著优势，例如减轻数据传输负担、提升网络管理效率以及优化资源分配。众多研究者已从网络架构、理论分析、潜在技术和未来应用等多个角度对语义通信进行了广泛探索。然而，随着语义通信的持续发展，大量安全与隐私问题随之浮现，对语义通信系统的机密性、完整性和可用性构成了威胁。本文对可用于保障语义通信安全的技术进行了全面综述。首先，我们详细阐述了语义通信的完整生命周期，包括模型训练、模型传输和语义信息传输三个阶段。接着，我们识别了在这三个阶段中出现的各类安全与隐私问题。此外，我们总结了可用于缓解这些安全与隐私威胁的技术，包括数据清洗、鲁棒学习、针对后门攻击的防御策略、对抗训练、差分隐私、密码学、区块链技术、模型压缩以及物理层安全。最后，本文概述了未来的研究方向，以引导相关领域的研究者。