On Protecting the Data Privacy of Large Language Models (LLMs): A Survey

On Protecting the Data Privacy of Large Language Models (LLMs): A Survey

14 Mar 2024 | Biwei Yan, Kun Li, Minghui Xu, Yueyan Dong, Yue Zhang, Zhaochun Ren, Xiuzhen Cheng
This paper provides a comprehensive survey of the data privacy concerns and protection mechanisms in Large Language Models (LLMs). LLMs, which are complex AI systems capable of understanding and generating human language, pose significant risks to data privacy due to their extensive processing of sensitive information. The paper categorizes these concerns into two main groups: passive privacy leakage and active privacy attacks. Passive privacy leakage occurs when users inadvertently expose sensitive data through their interactions with LLMs, while active privacy attacks involve exploiting vulnerabilities in the models to access or manipulate sensitive information. The paper also reviews various privacy protection mechanisms applied at different stages of LLM development, including pre-training, fine-tuning, and inference. These mechanisms include data sanitization, federated learning, differential privacy, homomorphic encryption, and secure multi-party computation. Each mechanism is evaluated for its effectiveness and constraints in protecting LLMs from privacy threats. Additionally, the paper discusses the challenges and future directions in LLM privacy protection, emphasizing the need for more advanced and context-specific solutions. The authors aim to provide a detailed framework for researchers and practitioners to enhance the privacy of LLMs, ensuring that these powerful tools are used responsibly and securely.This paper provides a comprehensive survey of the data privacy concerns and protection mechanisms in Large Language Models (LLMs). LLMs, which are complex AI systems capable of understanding and generating human language, pose significant risks to data privacy due to their extensive processing of sensitive information. The paper categorizes these concerns into two main groups: passive privacy leakage and active privacy attacks. Passive privacy leakage occurs when users inadvertently expose sensitive data through their interactions with LLMs, while active privacy attacks involve exploiting vulnerabilities in the models to access or manipulate sensitive information. The paper also reviews various privacy protection mechanisms applied at different stages of LLM development, including pre-training, fine-tuning, and inference. These mechanisms include data sanitization, federated learning, differential privacy, homomorphic encryption, and secure multi-party computation. Each mechanism is evaluated for its effectiveness and constraints in protecting LLMs from privacy threats. Additionally, the paper discusses the challenges and future directions in LLM privacy protection, emphasizing the need for more advanced and context-specific solutions. The authors aim to provide a detailed framework for researchers and practitioners to enhance the privacy of LLMs, ensuring that these powerful tools are used responsibly and securely.
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