Private Linear Regression

Private linear regression focuses on developing methods for performing linear regression while preserving the privacy of individual data points, typically using differential privacy as a rigorous framework. Current research emphasizes improving the accuracy and efficiency of private linear regression algorithms, exploring techniques like sufficient statistic perturbation, data-dependent mechanisms, and adaptive clipping to reduce the error introduced by privacy-preserving noise. These advancements are crucial for enabling the use of sensitive data in machine learning applications while upholding individual privacy guarantees, with implications for various fields including healthcare and finance.