@inproceedings{chen-etal-2023-customized,
    title = "A Customized Text Sanitization Mechanism with Differential Privacy",
    author = "Chen, Sai  and
      Mo, Fengran  and
      Wang, Yanhao  and
      Chen, Cen  and
      Nie, Jian-Yun  and
      Wang, Chengyu  and
      Cui, Jamie",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.355",
    doi = "10.18653/v1/2023.findings-acl.355",
    pages = "5747--5758",
    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 $\epsilon$-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 \url{https://github.com/sai4july/CusText}.",
}

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 $\epsilon$-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 \url{https://github.com/sai4july/CusText}.
Chen, Sai Mo, Fengran Wang, Yanhao Chen, Cen Nie, Jian-Yun Wang, Chengyu Cui, Jamie
A Customized Text Sanitization Mechanism with Differential Privacy