Benchmarking Synthetic Tabular Data: A Multi-Dimensional Evaluation Framework

• URL: http://arxiv.org/abs/2504.01908v1
• Date: Wed, 02 Apr 2025 17:10:30 GMT
• Title: Benchmarking Synthetic Tabular Data: A Multi-Dimensional Evaluation Framework
• Authors: Andrey Sidorenko, Michael Platzer, Mario Scriminaci, Paul Tiwald,
• Abstract summary: evaluating the quality of synthetic data remains a key challenge for ensuring privacy and utility in data-driven research.<n>We present a framework that quantifies how well synthetic data replicates original distributional properties while ensuring privacy.
• Score: 0.4874819476581695
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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