Synthetic Data Blueprint (SDB): A modular framework for the statistical, structural, and graph-based evaluation of synthetic tabular data
Vasileios C. Pezoulas, Nikolaos S. Tachos, Eleni Georga, Kostas Marias, Manolis Tsiknakis, and Dimitrios I. Fotiadis

TL;DR
The paper introduces SDB, a comprehensive Python library for evaluating synthetic tabular data quality using diverse metrics, visualizations, and domain-agnostic assessments across multiple real-world applications.
Contribution
It presents a modular, versatile framework that unifies various fidelity metrics and visualization tools for synthetic data evaluation, addressing fragmentation in current practices.
Findings
SDB effectively assesses data fidelity across diverse domains.
The framework supports automated feature detection and multiple evaluation metrics.
It demonstrates robustness and applicability in healthcare, finance, and cybersecurity contexts.
Abstract
In the rapidly evolving era of Artificial Intelligence (AI), synthetic data are widely used to accelerate innovation while preserving privacy and enabling broader data accessibility. However, the evaluation of synthetic data remains fragmented across heterogeneous metrics, ad-hoc scripts, and incomplete reporting practices. To address this gap, we introduce Synthetic Data Blueprint (SDB), a modular Pythonic based library to quantitatively and visually assess the fidelity of synthetic tabular data. SDB supports: (i) automated feature-type detection, (ii) distributional and dependency-level fidelity metrics, (iii) graph- and embedding-based structure preservation scores, and (iv) a rich suite of data visualization schemas. To demonstrate the breadth, robustness, and domain-agnostic applicability of the SDB, we evaluated the framework across three real-world use cases that differ…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Machine Learning in Healthcare · Artificial Intelligence in Healthcare and Education
