NAP^2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Human
<|reference_start|>NAP^2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Human: Increasing concerns about privacy leakage issues in academia and industry arise when employing NLP models from third-party providers to process sensitive texts. To protect privacy before sending sensitive data to those models, we suggest sanitizing sensitive text using two common strategies used by humans: i) deleting sensitive expressions, and ii) obscuring sensitive details by abstracting them. To explore the issues and develop a tool for text rewriting, we curate the first corpus, coined NAP^2, through both crowdsourcing and the use of large language models (LLMs). Compared to the prior works based on differential privacy, which lead to a sharp drop in information utility and unnatural texts, the human-inspired approaches result in more natural rewrites and offer an improved balance between privacy protection and data utility, as demonstrated by our extensive experiments.<|reference_end|>
@article{huang2024nap^2:,
    title={NAP^2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting
  by Learning from Human},
    author={Shuo Huang, William MacLean, Xiaoxi Kang, Anqi Wu, Lizhen Qu, Qiongkai
  Xu, Zhuang Li, Xingliang Yuan, Gholamreza Haffari},
    journal={arXiv preprint arXiv:2406.03749},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03749},
    primaryClass={cs.CL}
}