Should I use Synthetic Data for That? An Analysis of the Suitability of Synthetic Data for Data Sharing and Augmentation

2026-02-03

AI Summary

This study systematically evaluates the suitability and effectiveness of synthetic data across three canonical scenarios: data sharing, model training augmentation, and variance reduction in statistical estimation. By integrating formal modeling, theoretical analysis of generative models, and empirical case studies, the work presents the first comprehensive taxonomy of synthetic data applications and delineates their boundaries of applicability. The research elucidates both the potential and fundamental limitations of synthetic data in enhancing privacy preservation, model performance, and statistical stability. It further demonstrates that many existing or proposed use cases are misaligned with the intrinsic properties of synthetic data, thereby providing decision-makers with a principled theoretical framework to assess whether synthetic data is appropriate for addressing specific data availability challenges.

Technology Category

• Reasoning under Uncertainty: Applications
• Game Theory and Economic Paradigms: Applications
• Machine Learning: Privacy

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Recent advances in generative modelling have led many to see synthetic data as the go-to solution for a range of problems around data access, scarcity, and under-representation. In this paper, we study three prominent use cases: (1) Sharing synthetic data as a proxy for proprietary datasets to enable statistical analyses while protecting privacy, (2) Augmenting machine learning training sets with synthetic data to improve model performance, and (3) Augmenting datasets with synthetic data to reduce variance in statistical estimation. For each use case, we formalise the problem setting and study, through formal analysis and case studies, under which conditions synthetic data can achieve its intended objectives. We identify fundamental and practical limits that constrain when synthetic data can serve as an effective solution for a particular problem. Our analysis reveals that due to these limits many existing or envisioned use cases of synthetic data are a poor problem fit. Our formalisations and classification of synthetic data use cases enable decision makers to assess whether synthetic data is a suitable approach for their specific data availability problem.

Problem

• synthetic data
• data sharing
• data augmentation
• privacy
• statistical estimation

Innovation

• synthetic data
• data augmentation
• privacy-preserving sharing
• statistical estimation
• generative modeling