SynFD: Functional Dependency Repair for Tabular Synthesis

Tabular data synthesis has made substantial progress in improving distributional fidelity. However, for relational tables, which are standard in relational databases, statistically realistic samples are not necessarily structurally valid. For such tables, functional dependencies (FDs) specify deterministic relationships among attributes, which cannot be easily formulated or guaranteed by tabular generative objectives. Motivated by this observation, we propose SYNFD, a framework that repairs synthesized tabular data for FD consistency. SYNFD exploits mapping lookups for seen determinants (left-hand-side value of an FD), runs conditional generation for unseen determinants, and follows topological dependency propagation to process chained FDs. Experiments across multiple tabular datasets show that SYNFD substantially improves FD consistency, achieving an FD-consistent rate of 98.4% on average and a training-mapping agreement rate of 99.8%, while maintaining strong distributional fidelity and downstream utility.