Dependency-aware synthetic tabular data generation

Synthetic tabular data is increasingly used in privacy-sensitive domains such as healthcare, but existing generative models often fail to preserve inter-attribute relationships. In particular, functional dependencies (FDs) and logical dependencies (LDs), which capture deterministic and rule-based associations between features, are rarely or often poorly retained in synthetic datasets. To address this research gap, we propose the Hierarchical Feature Generation Framework (HFGF) for synthetic tabular data generation. We created benchmark datasets with known dependencies to evaluate our proposed HFGF. The framework first generates independent features using any standard generative model, and then reconstructs dependent features based on predefined FD and LD rules. Our experiments on four benchmark datasets and three publicly available real-world datasets with varying sizes, feature imbalance, and dependency complexity demonstrate that HFGF improves the preservation of FDs and LDs across six generative models, including CTGAN , TVAE , and GReaT . Utility analysis and qualitative dependency visualizations further show that HFGF significantly enhances the structural fidelity and utility of synthetic tabular data. 1