Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage

Md Rafi Ur Rashid, Jing Liu, Toshiaki Koike-Akino, Shagufta Mehnaz, Ye Wang

Keywords: Speech & Natural Language Processing (SNLP) -> SNLP: Ethics -- Bias, Fairness, Transparency & Privacy, Speech & Natural Language Processing (SNLP) -> SNLP: Safety and Robustness

TL;DR: Poisoning pre-trained language models enhances membership inference and data extraction attacks

Abstract: 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.

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Submission Number: 15