The rapid advancement of large language models (LLMs) has revolutionized natural language processing, enabling applications in diverse domains such as healthcare, finance and education. However, the growing reliance on extensive data for training and inference has raised significant privacy concerns, ranging from data leakage to adversarial attacks. This survey comprehensively explores the landscape of privacy-preserving mechanisms tailored for LLMs, including differential privacy, federated learning, cryptographic protocols, and trusted execution environments. We examine their efficacy in addressing key privacy challenges, such as membership inference and model inversion attacks, while balancing trade-offs between privacy and model utility. Furthermore, we analyze privacy-preserving applications of LLMs in privacy-sensitive domains, highlighting successful implementations and inherent limitations. Finally, this survey identifies emerging research directions, emphasizing the need for novel frameworks that integrate privacy by design into the lifecycle of LLMs. By synthesizing state-of-the-art approaches and future trends, this paper provides a foundation for developing robust, privacy-preserving large language models that safeguard sensitive information without compromising performance.

翻译：大语言模型（LLMs）的快速发展彻底改变了自然语言处理领域，使其在医疗健康、金融和教育等多个领域得到广泛应用。然而，模型训练和推理对大规模数据的日益依赖引发了严重的隐私担忧，包括数据泄露和对抗攻击等问题。本综述全面探讨了针对LLMs的隐私保护机制，包括差分隐私、联邦学习、密码学协议和可信执行环境。我们评估了这些机制在应对成员推理攻击和模型反演攻击等关键隐私挑战方面的有效性，并分析了隐私保护与模型效用之间的权衡。此外，我们分析了LLMs在隐私敏感领域的隐私保护应用，重点介绍了成功案例和固有局限性。最后，本文指出了新兴的研究方向，强调需要构建将隐私设计原则融入LLMs全生命周期的新型框架。通过综合前沿方法和未来趋势，本文为开发既能保护敏感信息又不影响性能的鲁棒隐私保护大语言模型奠定了基础。