Identification of Potential Advancements and Improvements:
1. Personalized Privacy Guarantees: Techniques that allow individuals to specify their desired privacy levels, leading to personalized privacy guarantees that align with users’ preferences.
2. Dynamic Budget Allocation: Developing strategies for dynamically allocating the privacy budget based on query characteristics and data sensitivity, adapting to evolving data analysis needs.
3. Contextual Composition: Investigating how context-specific information can be integrated into composition algorithms to offer more accurate privacy bounds in specific scenarios.
4. Machine Learning-Enhanced Composition: Exploring how machine learning models can assist in optimizing composition strategies by considering historical analysis outcomes and mechanism behaviours.