Private Tabular Data

Private tabular data research focuses on generating synthetic tabular datasets that preserve individual privacy while maintaining data utility, crucial for responsible data sharing. Current efforts leverage deep learning architectures, including generative adversarial networks (GANs) and transformer-based models, often incorporating differential privacy mechanisms to provide formal privacy guarantees. These methods are evaluated against traditional marginal-based approaches, with a growing emphasis on achieving high data utility under stringent privacy constraints. This field is significant for enabling data analysis and machine learning on sensitive data while mitigating privacy risks, impacting various sectors requiring data sharing with privacy protections.