Private Estimation

Private estimation focuses on developing algorithms that accurately estimate statistical properties of data while rigorously protecting individual privacy, typically using differential privacy frameworks. Current research emphasizes improving the efficiency and accuracy of private estimators for various models, including linear regression, Gaussian distributions, and Hawkes processes, often employing techniques like noise injection, exponential mechanisms, and adaptive algorithms that leverage data structure to minimize privacy loss. These advancements are crucial for enabling data analysis and machine learning in sensitive domains like healthcare and social science, where privacy is paramount, while maintaining statistical utility.