Privacy in Fine-tuning Large Language Models: Attacks, Defenses, and Future Directions

• URL: http://arxiv.org/abs/2412.16504v1
• Date: Sat, 21 Dec 2024 06:41:29 GMT
• Title: Privacy in Fine-tuning Large Language Models: Attacks, Defenses, and Future Directions
• Authors: Hao Du, Shang Liu, Lele Zheng, Yang Cao, Atsuyoshi Nakamura, Lei Chen,
• Abstract summary: Fine-tuning Large Language Models (LLMs) can achieve state-of-the-art performance across various domains. This paper provides a comprehensive survey of privacy challenges associated with fine-tuning LLMs. We highlight vulnerabilities to various privacy attacks, including membership inference, data extraction, and backdoor attacks.
• Score: 11.338466798715906
• License:
• Abstract: Fine-tuning has emerged as a critical process in leveraging Large Language Models (LLMs) for specific downstream tasks, enabling these models to achieve state-of-the-art performance across various domains. However, the fine-tuning process often involves sensitive datasets, introducing privacy risks that exploit the unique characteristics of this stage. In this paper, we provide a comprehensive survey of privacy challenges associated with fine-tuning LLMs, highlighting vulnerabilities to various privacy attacks, including membership inference, data extraction, and backdoor attacks. We further review defense mechanisms designed to mitigate privacy risks in the fine-tuning phase, such as differential privacy, federated learning, and knowledge unlearning, discussing their effectiveness and limitations in addressing privacy risks and maintaining model utility. By identifying key gaps in existing research, we highlight challenges and propose directions to advance the development of privacy-preserving methods for fine-tuning LLMs, promoting their responsible use in diverse applications.

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