Private Data

Private data research focuses on enabling the use of sensitive information for machine learning and other data analysis tasks while rigorously protecting individual privacy. Current efforts concentrate on developing differentially private algorithms, federated learning frameworks (including variations like vertical federated learning and split learning), and techniques for synthetic data generation to mitigate privacy risks associated with model training and data sharing. These advancements are crucial for unlocking the potential of large datasets in various fields, from healthcare and finance to social sciences, while addressing ethical and legal concerns surrounding data privacy.