NAP^2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Human

• URL: http://arxiv.org/abs/2406.03749v1
• Date: Thu, 6 Jun 2024 05:07:44 GMT
• Title: NAP^2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Human
• Authors: Shuo Huang, William MacLean, Xiaoxi Kang, Anqi Wu, Lizhen Qu, Qiongkai Xu, Zhuang Li, Xingliang Yuan, Gholamreza Haffari,
• Abstract summary: We suggest sanitizing sensitive text using two common strategies used by humans. We curate the first corpus, coined NAP2, through both crowdsourcing and the use of large language models.
• Score: 55.20137833039499
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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