User Configurable Privacy Defense

User-configurable privacy defense aims to protect sensitive data used in machine learning while maintaining model utility, addressing the inherent trade-off between privacy and accuracy. Current research focuses on developing methods that allow users to specify their individual privacy preferences, employing techniques like differential privacy with varying granularities (e.g., sentence vs. document level) and incorporating domain knowledge into synthetic data generation to improve realism and reduce privacy risks. These advancements are crucial for responsible data sharing and deployment of machine learning models in sensitive domains like healthcare and cybersecurity, enabling more nuanced and effective privacy controls tailored to specific data and application needs.