Differentially Private Natural Language Models: Recent Advances and Future Directions

Understand

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 protecting the 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.