Private Text

Private text research focuses on developing methods to protect sensitive information within textual data while maintaining data utility. Current efforts concentrate on differentially private text rewriting using masked language models and other techniques like federated learning and secure multi-party computation to enable collaborative model training without compromising individual privacy. This field is crucial for addressing privacy concerns in various applications, from smart grids and healthcare to online interactions, and is driving advancements in both privacy-preserving machine learning algorithms and the development of robust evaluation methods. The development of large, annotated datasets of private text is also a key area of focus, enabling better model training and evaluation.