Synthetic Data: A Tool for Privacy Protection and Model Empowerment
TL;DR
This work discusses key considerations and methods for generating synthetic data, including sequential modeling, differentially private synthesis, deep generative models, and large language models, and outlines some open research challenges and future directions for synthetic data development.
Abstract
Synthetic data, i.e., data simulated from some statistical model, are an important tool for both privacy protection and artificial intelligence pipelines. In the privacy context, synthetic data enable agencies to disseminate record-level information while reducing disclosure risks. In the artificial intelligence context, synthetic data allow analysts to augment training sets, increase coverage of rare cases, and support experimentation when genuine data are scarce. For each usage, we discuss key considerations and methods for generating synthetic data, including sequential modeling, differentially private synthesis, deep generative models, and large language models. Throughout, we highlight key trade-offs between data usefulness, privacy protection, and model reliability. We conclude by outlining some open research challenges and future directions for synthetic data development.