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- W4363671870 abstract "Prompt-tuning has received attention as an efficient tuning method in the language domain, i.e., tuning a prompt that is a few tokens long, while keeping the large language model frozen, yet achieving comparable performance with conventional fine-tuning. Considering the emerging privacy concerns with language models, we initiate the study of privacy leakage in the setting of prompt-tuning. We first describe a real-world email service pipeline to provide customized output for various users via prompt-tuning. Then we propose a novel privacy attack framework to infer users' private information by exploiting the prompt module with user-specific signals. We conduct a comprehensive privacy evaluation on the target pipeline to demonstrate the potential leakage from prompt-tuning. The results also demonstrate the effectiveness of the proposed attack." @default.
- W4363671870 created "2023-04-11" @default.
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- W4363671870 date "2023-04-07" @default.
- W4363671870 modified "2023-09-27" @default.
- W4363671870 title "Does Prompt-Tuning Language Model Ensure Privacy?" @default.
- W4363671870 doi "https://doi.org/10.48550/arxiv.2304.03472" @default.
- W4363671870 hasPublicationYear "2023" @default.
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