Generate Complex Synthetic Tabular Data with Navigator Fine Tuning + Differential Privacy

Navigator Fine Tuning (NavFT) is a model used by Gretel for generating synthetic tabular datasets containing various types of fields. The company has now enabled fine-tuning with differential privacy (DP), which provides strong, formal guarantees against the leakage of sensitive information about the original dataset. Gretel supports three models with DP: NavFT, Gretel GPT, and Tabular DP. NavFT is ideal for tabular datasets with mixed column types, while Gretel GPT is best for free-text-only datasets, and Tabular DP is suited for numerical and categorical-only datasets. The use of differential privacy often reduces synthetic data quality, requiring a balance between privacy and utility. Experiments show that employing DP boosts the Data Privacy Score while decreasing the Synthetic Quality Score.