Privacy Mechanism

Privacy mechanisms aim to protect sensitive data used in machine learning, particularly in applications like federated learning and large language models, by balancing data utility with privacy preservation. Current research focuses on developing and analyzing techniques like differential privacy (including its variants such as ranked differential privacy), secure aggregation, and adversarial training, often employing neural network architectures for data transformation and privacy-preserving model training. These advancements are crucial for building trustworthy AI systems and enabling responsible data sharing across various domains, addressing concerns about data breaches and individual privacy violations.