arXiv:2412.16504v1 Announce Type: new 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.
The article “Privacy Challenges in Fine-Tuning Large Language Models: Vulnerabilities and Defense Mechanisms” explores the critical process of fine-tuning Large Language Models (LLMs) for specific tasks and the associated privacy risks. While fine-tuning enables LLMs to achieve state-of-the-art performance, it often involves sensitive datasets, making it susceptible to privacy attacks. The paper provides a comprehensive survey of these privacy challenges, including membership inference, data extraction, and backdoor attacks. It also reviews defense mechanisms like differential privacy, federated learning, and knowledge unlearning, discussing their effectiveness and limitations. By identifying gaps in existing research, the article proposes directions to advance privacy-preserving methods for fine-tuning LLMs, promoting responsible use in various applications.
Exploring Privacy Challenges in Fine-Tuning Large Language Models
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 their paper, the authors 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. These attacks target the privacy of users and the confidentiality of their data.
The authors further review defense mechanisms designed to mitigate privacy risks in the fine-tuning phase. One such mechanism is differential privacy, which aims to provide privacy guarantees by adding noise to the training process or perturbing the data. Another approach is federated learning, where the training data remains on user devices, and only model updates are shared, safeguarding user privacy. Additionally, knowledge unlearning techniques aim to remove certain information from the model to prevent unintended leakage of sensitive data.
While these defense mechanisms offer promising solutions, they also have limitations. Differential privacy may incur a trade-off between privacy and model utility, as noise addition can affect the model’s performance. Federated learning relies on user devices being trustworthy and assumes that adversaries cannot compromise a significant portion of the devices. Knowledge unlearning techniques also face challenges in identifying and removing all sensitive information from the model.
By identifying key gaps in existing research, the authors highlight challenges and propose directions to advance the development of privacy-preserving methods for fine-tuning LLMs. They emphasize the importance of promoting responsible use of these models in diverse applications.
Overall, this paper sheds light on the privacy risks associated with fine-tuning LLMs and provides insights into potential defense mechanisms. It serves as a call to action for researchers and practitioners to further explore and develop privacy-preserving methods that can ensure the responsible use of LLMs while maintaining user privacy and data confidentiality.
The paper titled “Privacy Challenges in Fine-Tuning Large Language Models” provides a comprehensive survey of the privacy risks associated with the fine-tuning process of Large Language Models (LLMs). Fine-tuning has become a crucial step in leveraging LLMs for specific tasks and has enabled these models to achieve state-of-the-art performance across various domains. However, the authors highlight the potential privacy vulnerabilities that arise during this stage.
One of the key privacy risks discussed in the paper is membership inference. Membership inference attacks aim to determine whether a specific data sample was part of the fine-tuning dataset. This can be a concern when the fine-tuning process involves sensitive or private data. The authors also highlight the risk of data extraction, where an attacker tries to extract specific information from the fine-tuned model, potentially revealing sensitive details about the training data.
Another privacy risk discussed in the paper is backdoor attacks. These attacks involve injecting a specific trigger or pattern into the fine-tuned model, which can be exploited to manipulate the model’s behavior in unintended ways. Backdoor attacks pose a significant threat, especially when fine-tuning is performed with untrusted or adversarial datasets.
To address these privacy risks, the paper reviews several defense mechanisms that can be employed during the fine-tuning process. One such mechanism is differential privacy, which aims to protect individual data samples by adding noise during the fine-tuning process. Federated learning is another approach discussed, where the fine-tuning is performed locally on user devices, preserving privacy by not sharing raw data. Knowledge unlearning, which involves removing specific information from the fine-tuned model, is also explored as a potential defense mechanism.
The effectiveness and limitations of these defense mechanisms are analyzed in the paper. While differential privacy and federated learning show promise in preserving privacy during fine-tuning, they may come with trade-offs in terms of model performance or utility. Knowledge unlearning, on the other hand, may be effective in removing sensitive information but can also lead to a loss of useful knowledge.
The paper concludes by identifying key gaps in existing research and proposing directions to advance the development of privacy-preserving methods for fine-tuning LLMs. The authors emphasize the importance of responsible use of LLMs in diverse applications and highlight the need for further research to address the privacy challenges associated with fine-tuning.
In summary, this paper sheds light on the privacy risks involved in the fine-tuning process of Large Language Models and provides a comprehensive survey of defense mechanisms. By highlighting the gaps in current research, it paves the way for future developments in privacy-preserving methods for fine-tuning LLMs, promoting their responsible and secure use.
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