Recent developments in deep learning have led to great success in various
natural language processing (NLP) tasks. However, these applications may
involve data that contain sensitive information. Therefore, how to achieve good
performance while also protect privacy of sensitive data is a crucial challenge
in NLP. To preserve privacy, Differential Privacy (DP), which can prevent
reconstruction attacks and protect against potential side knowledge, is
becoming a de facto technique for private data analysis. In recent years, NLP
in DP models (DP-NLP) has been studied from different perspectives, which
deserves a comprehensive review. In this paper, we provide the first systematic
review of recent advances on DP deep learning models in NLP. In particular, we
first discuss some differences and additional challenges of DP-NLP compared
with the standard DP deep learning. Then we investigate some existing work on
DP-NLP and present its recent developments from two aspects: gradient
perturbation based methods and embedding vector perturbation based methods. We
also discuss some challenges and future directions of this topic.

该研究综述了近年来在自然语言处理（NLP）领域中，如何在保护敏感数据的隐私同时实现良好性能的关键挑战。为了保护数据隐私， 差分隐私（DP）成为了隐私数据分析的有效技术。本文着重探讨了在 DP 深度学习模型中的自然语言处理 (DP-NLP) 的最新研究进展，并阐述了一些挑战和未来方向。