@unpublished{xie2023does,
author = {Xie, Shangyu and Dai, Wei and Ghosh, Esha and Roy, Sambuddha and Schwartz, Dan and Laine, Kim},
title = {Does Prompt-Tuning Language Model Ensure Privacy?},
year = {2023},
month = {April},
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 pro pose 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.},
url = {http://approjects.co.za/?big=en-us/research/publication/does-prompt-tuning-language-model-ensure-privacy/},
}