Gaussian Mechanism

The Gaussian mechanism is a core technique in differential privacy, adding Gaussian noise to query outputs to protect sensitive data while preserving utility. Current research focuses on optimizing its application in various settings, including distributed mean estimation, private gradient descent, and matrix factorization, often exploring variations like the sampled Gaussian mechanism and correlated Gaussian noise to improve privacy-utility trade-offs. These advancements are crucial for enabling privacy-preserving machine learning and data analysis in diverse applications, particularly in federated learning and healthcare, where rigorous privacy guarantees are paramount.