Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage
<|reference_start|>Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage: Fine-tuning large language models on private data for downstream applications poses significant privacy risks in potentially exposing sensitive information. Several popular community platforms now offer convenient distribution of a large variety of pre-trained models, allowing anyone to publish without rigorous verification. This scenario creates a privacy threat, as pre-trained models can be intentionally crafted to compromise the privacy of fine-tuning datasets. In this study, we introduce a novel poisoning technique that uses model-unlearning as an attack tool. This approach manipulates a pre-trained language model to increase the leakage of private data during the fine-tuning process. Our method enhances both membership inference and data extraction attacks while preserving model utility. Experimental results across different models, datasets, and fine-tuning setups demonstrate that our attacks significantly surpass baseline performance. This work serves as a cautionary note for users who download pre-trained models from unverified sources, highlighting the potential risks involved.<|reference_end|>
@article{rashid2024forget,
    title={Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language
  Models for Privacy Leakage},
    author={Md Rafi Ur Rashid, Jing Liu, Toshiaki Koike-Akino, Shagufta Mehnaz, Ye
  Wang},
    journal={arXiv preprint arXiv:2408.17354},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.17354},
    primaryClass={cs.LG cs.AI cs.CR}
}