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    <abstract>As privacy issues are receiving increasing attention within the Natural Language Processing (NLP) community, numerous methods have been proposed to sanitize texts subject to differential privacy. However, the state-of-the-art text sanitization mechanisms based on a relaxed notion of metric local differential privacy (MLDP) do not apply to non-metric semantic similarity measures and cannot achieve good privacy-utility trade-offs. To address these limitations, we propose a novel Customized Text sanitization (CusText) mechanism based on the original ε-differential privacy (DP) definition, which is compatible with any similarity measure.Moreover, CusText assigns each input token a customized output set to provide more advanced privacy protection at the token level.Extensive experiments on several benchmark datasets show that CusText achieves a better trade-off between privacy and utility than existing mechanisms.The code is available at https://github.com/sai4july/CusText.</abstract>
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%0 Conference Proceedings
%T A Customized Text Sanitization Mechanism with Differential Privacy
%A Chen, Sai
%A Mo, Fengran
%A Wang, Yanhao
%A Chen, Cen
%A Nie, Jian-Yun
%A Wang, Chengyu
%A Cui, Jamie
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F chen-etal-2023-customized
%X As privacy issues are receiving increasing attention within the Natural Language Processing (NLP) community, numerous methods have been proposed to sanitize texts subject to differential privacy. However, the state-of-the-art text sanitization mechanisms based on a relaxed notion of metric local differential privacy (MLDP) do not apply to non-metric semantic similarity measures and cannot achieve good privacy-utility trade-offs. To address these limitations, we propose a novel Customized Text sanitization (CusText) mechanism based on the original ε-differential privacy (DP) definition, which is compatible with any similarity measure.Moreover, CusText assigns each input token a customized output set to provide more advanced privacy protection at the token level.Extensive experiments on several benchmark datasets show that CusText achieves a better trade-off between privacy and utility than existing mechanisms.The code is available at https://github.com/sai4july/CusText.
%R 10.18653/v1/2023.findings-acl.355
%U https://aclanthology.org/2023.findings-acl.355
%U https://doi.org/10.18653/v1/2023.findings-acl.355
%P 5747-5758

Markdown (Informal)

[A Customized Text Sanitization Mechanism with Differential Privacy](https://aclanthology.org/2023.findings-acl.355) (Chen et al., Findings 2023)

• A Customized Text Sanitization Mechanism with Differential Privacy (Chen et al., Findings 2023)

ACL

• Sai Chen, Fengran Mo, Yanhao Wang, Cen Chen, Jian-Yun Nie, Chengyu Wang, and Jamie Cui. 2023. A Customized Text Sanitization Mechanism with Differential Privacy. In Findings of the Association for Computational Linguistics: ACL 2023, pages 5747–5758, Toronto, Canada. Association for Computational Linguistics.