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2024

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

Yan, Biwei, Li, Kun, Xu, Minghui et al.

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Large language models (LLMs) are complex artificial intelligence systems capable of understanding, generating and translating human language.

They learn language patterns by analyzing large amounts of text data, allowing them to perform writing, conversation, summarizing and other language tasks.
When LLMs process and generate large amounts of data, there is a risk of leaking sensitive information, which may threaten data privacy.
This paper concentrates on elucidating the data privacy concerns associated with LLMs to foster a comprehensive understanding.

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

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M. Xu, D. Cai, Y. Wu, X. Li, and S. Wang, “Fwdllm: Efficient fedllm using forward gradient,” 2024

2024

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J. Zhang, S. Vahidian, M. Kuo, C. Li, R. Zhang, T. Yu, Y. Zhou, G. Wang, and Y. Chen, “Towards building the federated gpt: Federated instruction tuning,” 2024

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S. Liu, Y. Yao, J. Jia, S. Casper, N. Baracaldo, P. Hase, X. Xu, Y. Yao, H. Li, K. R. Varshney, M. Bansal, S. Koyejo, and Y. Liu, “Rethinking machine unlearning for large language models,” 2024

2024

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W. Huang, Y. Wang, A. Cheng, A. Zhou, C. Yu, and L. Wang, “A fast, performant, secure distributed training framework for large language model,” arXiv preprint arXiv:2401.09796 , 2024

Original

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J. Evertz, M. Chlosta, L. Schönherr, and T. Eisenhofer, “Whispers in the machine: Confidentiality in llm-integrated systems,” arXiv preprint arXiv:2402.06922 , 2024

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