Inside the Black Box: Detecting Data Leakage in Pre-trained Language Encoders
<|reference_start|>Inside the Black Box: Detecting Data Leakage in Pre-trained Language Encoders: Despite being prevalent in the general field of Natural Language Processing (NLP), pre-trained language models inherently carry privacy and copyright concerns due to their nature of training on large-scale web-scraped data. In this paper, we pioneer a systematic exploration of such risks associated with pre-trained language encoders, specifically focusing on the membership leakage of pre-training data exposed through downstream models adapted from pre-trained language encoders-an aspect largely overlooked in existing literature. Our study encompasses comprehensive experiments across four types of pre-trained encoder architectures, three representative downstream tasks, and five benchmark datasets. Intriguingly, our evaluations reveal, for the first time, the existence of membership leakage even when only the black-box output of the downstream model is exposed, highlighting a privacy risk far greater than previously assumed. Alongside, we present in-depth analysis and insights toward guiding future researchers and practitioners in addressing the privacy considerations in developing pre-trained language models.<|reference_end|>
@article{xin2024inside,
    title={Inside the Black Box: Detecting Data Leakage in Pre-trained Language
  Encoders},
    author={Yuan Xin, Zheng Li, Ning Yu, Dingfan Chen, Mario Fritz, Michael Backes
  and Yang Zhang},
    journal={arXiv preprint arXiv:2408.11046},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11046},
    primaryClass={cs.CL}
}