  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 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 $\epsilon$-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. The code is available at https://github.com/sai4july/CusText.
