A Customized Text Sanitization Mechanism with Differential Privacy
Sai Chen, Fengran Mo, Yanhao Wang, Cen Chen, Jian‐Yun Nie, Chengyu Wang, Jamie Cui · 2023
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-theart text sanitization mechanisms based on metric local differential privacy (MLDP) do not apply to non-metric semantic similarity measures and cannot achieve good trade-offs between privacy and utility.To address the above limitations, we propose a novel Customized Text (CusText) sanitization mechanism based on the original ϵ-differential privacy (DP) definition, which is compatible with any similarity measure.Furthermore, CusText assigns each input token a customized output set of tokens 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.