Privacy Guarantee

Privacy guarantees in machine learning aim to enable collaborative model training and data analysis while protecting sensitive individual information. Current research focuses on enhancing differential privacy mechanisms, particularly within federated learning frameworks, employing techniques like noise addition, gradient clipping, and model compression to achieve a balance between privacy and model utility. These advancements are crucial for responsible data usage in various fields, including healthcare, finance, and social sciences, facilitating the development of powerful models while mitigating privacy risks. Active research also explores alternative approaches like homomorphic encryption and synthetic data generation to provide robust privacy protections.