Papers › Benchmarking Synthetic Tabular Data: A Multi-Dimensional Evaluation Framework

Benchmarking Synthetic Tabular Data: A Multi-Dimensional Evaluation Framework

2 Apr 2025arXiv:2504.01908archive 2025-07-28

Andrey Sidorenko, Michael Platzer, Mario Scriminaci, Paul Tiwald

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 at https://github.com/mostly-ai/mostlyai-qa.

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