Synthetic Data Vault

Synthetic Data Vaults (SDVs) are systems designed to generate realistic synthetic datasets, addressing the need for privacy-preserving data sharing and model training. Current research focuses on improving the quality and diversity of synthetic data, particularly for sequential data, using techniques like conditional probabilistic autoregressive models and comparing their performance against generative adversarial networks. These advancements are significant because high-quality synthetic data enables the development and evaluation of machine learning models, especially in sensitive domains like software security, where real data is often restricted, while also facilitating code understanding and generation tasks through large language model training.