  Privacy preservation remains a key challenge in data mining and Natural
Language Understanding (NLU). Previous research shows that the input text or
even text embeddings can leak private information. This concern motivates our
research on effective privacy preservation approaches for pretrained Language
Models (LMs). We investigate the privacy and utility implications of applying
dx-privacy, a variant of Local Differential Privacy, to BERT fine-tuning in NLU
applications. More importantly, we further propose privacy-adaptive LM
pretraining methods and show that our approach can boost the utility of BERT
dramatically while retaining the same level of privacy protection. We also
quantify the level of privacy preservation and provide guidance on privacy
configuration. Our experiments and findings lay the groundwork for future
explorations of privacy-preserving NLU with pretrained LMs.
