Benchmarking Synthetic Tabular Data: A Multi-Dimensional Evaluation Framework

2025-04-02

AI Summary

To address the challenge of balancing privacy preservation and utility in synthetic tabular data evaluation, this paper proposes the first standardized, multidimensional assessment framework. Methodologically, it integrates low- and high-dimensional distributional comparisons, deep embedding similarity metrics, k-nearest-neighbor distance statistics, and statistical distribution tests—establishing a unified benchmark paradigm covering statistical utility, privacy security, and interpretable diagnostics, applicable to both sequential and context-structured data. Contributions include: (1) a quantifiable, interpretable, and cross-method comparable quality evaluation system; (2) adoption of a hold-out benchmarking strategy with standardized metrics, significantly enhancing reproducibility and assessment consistency; and (3) open-sourcing of the implementation to foster methodological unification in the field.

Technology Category

Machine Learning: Evaluation and AnalysisNatural Language Processing: Safety and RobustnessSearch and Optimization: Evaluation and Analysis

Application Category

Search and Retrieval-Augmented AI: Web evaluation methodologies and metricsSecurity and Privacy: Large-scale security measurementsEconomics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• Evaluating synthetic data quality for privacy and utility
• Developing a multi-dimensional benchmarking framework for synthetic data
• Ensuring reproducibility in synthetic data generation techniques

Innovation

• Holdout-based benchmarking strategy for evaluation
• Multi-dimensional distribution and similarity metrics
• Standardized metrics for interpretable quality diagnostics