Benchmarking Synthetic Tabular Data: A Multi-Dimensional Evaluation Framework

Evaluating the quality of synthetic data remains a key challenge for ensuring privacy and utility in data-driven research. In this work, we present an evaluation framework that quantifies how well synthetic data replicates original distributional properties while ensuring privacy. The proposed approach employs a holdout-based benchmarking strategy that facilitates quantitative assessment through low- and high-dimensional distribution comparisons, embedding-based similarity measures, and nearest-neighbor distance metrics. The framework supports various data types and structures, including sequential and contextual information, and enables interpretable quality diagnostics through a set of standardized metrics. These contributions aim to support reproducibility and methodological consistency in benchmarking of synthetic data generation techniques. The code of the framework is available atthis https URL.

@article{sidorenko2025_2504.01908,
  title={ Benchmarking Synthetic Tabular Data: A Multi-Dimensional Evaluation Framework },
  author={ Andrey Sidorenko and Michael Platzer and Mario Scriminaci and Paul Tiwald },
  journal={arXiv preprint arXiv:2504.01908},
  year={ 2025 }
}