Private Learning

Private learning aims to train machine learning models on sensitive data while preserving individual privacy, primarily using techniques like differential privacy and federated learning. Current research focuses on improving the accuracy and efficiency of private learning algorithms, exploring methods such as buffered linear Toeplitz mechanisms, adaptive clipping, and the incorporation of public data or pre-trained models to enhance utility. This field is crucial for enabling the use of sensitive data in machine learning applications across various domains, while mitigating privacy risks and fostering trust.