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        <title>Robust Utility-Preserving Text Anonymization Based on Large Language Models</title>
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        <namePart type="given">Tianyu</namePart>
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    <abstract>Anonymizing text that contains sensitive information is crucial for a wide range of applications. Existing techniques face the emerging challenges of the re-identification ability of large language models (LLMs), which have shown advanced capability in memorizing detailed information and reasoning over dispersed pieces of patterns to draw conclusions. When defending against LLM-based re-identification, anonymization could jeopardize the utility of the resulting anonymized data in downstream tasks. In general, the interaction between anonymization and data utility requires a deeper understanding within the context of LLMs. In this paper, we propose a framework composed of three key LLM-based components: a privacy evaluator, a utility evaluator and an optimization component, which work collaboratively to perform anonymization. Extensive experiments demonstrate that the proposed model outperforms existing baselines, showing robustness in reducing the risk of re-identification while preserving greater data utility in downstream tasks. We provide detailed studies on these core modules. To consider large-scale and real-time applications, we investigate the distillation of the anonymization capabilities into lightweight models. All of our code and datasets will be made publicly available at [Github URL].</abstract>
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    <identifier type="doi">10.18653/v1/2025.acl-long.1404</identifier>
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%0 Conference Proceedings
%T Robust Utility-Preserving Text Anonymization Based on Large Language Models
%A Yang, Tianyu
%A Zhu, Xiaodan
%A Gurevych, Iryna
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F yang-etal-2025-robust
%X Anonymizing text that contains sensitive information is crucial for a wide range of applications. Existing techniques face the emerging challenges of the re-identification ability of large language models (LLMs), which have shown advanced capability in memorizing detailed information and reasoning over dispersed pieces of patterns to draw conclusions. When defending against LLM-based re-identification, anonymization could jeopardize the utility of the resulting anonymized data in downstream tasks. In general, the interaction between anonymization and data utility requires a deeper understanding within the context of LLMs. In this paper, we propose a framework composed of three key LLM-based components: a privacy evaluator, a utility evaluator and an optimization component, which work collaboratively to perform anonymization. Extensive experiments demonstrate that the proposed model outperforms existing baselines, showing robustness in reducing the risk of re-identification while preserving greater data utility in downstream tasks. We provide detailed studies on these core modules. To consider large-scale and real-time applications, we investigate the distillation of the anonymization capabilities into lightweight models. All of our code and datasets will be made publicly available at [Github URL].
%R 10.18653/v1/2025.acl-long.1404
%U https://aclanthology.org/2025.acl-long.1404/
%U https://doi.org/10.18653/v1/2025.acl-long.1404
%P 28922-28941

Markdown (Informal)

[Robust Utility-Preserving Text Anonymization Based on Large Language Models](https://aclanthology.org/2025.acl-long.1404/) (Yang et al., ACL 2025)

• Robust Utility-Preserving Text Anonymization Based on Large Language Models (Yang et al., ACL 2025)

ACL

• Tianyu Yang, Xiaodan Zhu, and Iryna Gurevych. 2025. Robust Utility-Preserving Text Anonymization Based on Large Language Models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 28922–28941, Vienna, Austria. Association for Computational Linguistics.