The widespread use of cloud-based Large Language Models (LLMs) has heightened concerns over user privacy, as sensitive information may be inadvertently exposed during interactions with these services. 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 on anonymization, 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. Researchers interested in accessing the dataset are encouraged to contact the first or corresponding author via email.
@article{arxiv.2406.03749,
title = {NAP^2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Human},
author = {Shuo Huang and William MacLean and Xiaoxi Kang and Qiongkai Xu and Zhuang Li and Xingliang Yuan and Gholamreza Haffari and Lizhen Qu},
journal= {arXiv preprint arXiv:2406.03749},
year = {2025}
}