When trying to apply the recent advance of Natural Language Understanding (NLU) technologies to real-world applications, privacy preservation imposes a crucial challenge, which, unfortunately, has not been well resolved. To address this issue, we study how to improve the effectiveness of NLU models under a Local Privacy setting, using BERT, a widely-used pretrained Language Model (LM), as an example. We systematically study the strengths and weaknesses of imposing dx-privacy, a relaxed variant of Local Differential Privacy, at different stages of language modeling: input text, token embeddings, and sequence representations. We then focus on the former two with privacy-constrained fine-tuning experiments to reveal the utility of BERT under local privacy constraints. More importantly, to the best of our knowledge, we are the first to propose privacy-adaptive LM pretraining methods and demonstrate that they can significantly improve model performance on privatized text input. We also interpret the level of privacy preservation and provide our guidance on privacy parameter selections.

在自然语言理解和数据挖掘中，隐私保护仍然是一个关键的挑战。本文研究了对预先训练的语言模型应用局部差分隐私的隐私保护方法，并指出了这种方法在BERT细调中的实用性和隐私性的影响，同时提出了隐私适应性的语言模型预训练方法，可以显著提高BERT的实用性，并保持相同水平的隐私保护。本文实验和发现为未来探索隐私保护的自然语言理解提供了奠基。

隐私保护BERT的自然语言理解