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arXiv · 2508.16254

FEST: A Unified Framework for Evaluating Synthetic Tabular Data

Weijie Niu·Alberto Huertas Celdran·Karoline Siarsky·Burkhard Stiller
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Abstract

Synthetic data generation, leveraging generative machine learning techniques, offers a promising approach to mitigating privacy concerns associated with real-world data usage. Synthetic data closely resembles real-world data while maintaining strong privacy guarantees. However, a comprehensive assessment framework is still missing in the evaluation of synthetic data generation, especially when considering the balance between privacy preservation and data utility in synthetic data. This research bridges this gap by proposing FEST, a systematic framework for evaluating synthetic tabular data. FEST integrates diverse privacy metrics (attack-based and distance-based), along with similarity and machine learning utility metrics, to provide a holistic assessment. We develop FEST as an open-source Python-based library and validate it on multiple datasets, demonstrating its effectiveness in analyzing the privacy-utility trade-off of different synthetic data generation models. The source code of FEST is available on Github.

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Weijie Niu, Alberto Huertas Celdran, Karoline Siarsky, Burkhard Stiller. 2025-08-22. FEST: A Unified Framework for Evaluating Synthetic Tabular Data. https://arxiv.org/abs/2508.16254

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