Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Era

2025-08-27

AI Summary

To address core challenges in data mining—including data scarcity, privacy sensitivity, and high annotation costs—this paper proposes a task-oriented synthetic data generation paradigm. Methodologically, it systematically integrates state-of-the-art generative models—large language models, diffusion models, and generative adversarial networks—within a unified evaluation framework and reusable practical guidelines. Key contributions include: (i) the first coordinated application of multimodal generative models to data mining tasks, jointly optimizing data fidelity, statistical utility, and privacy preservation; (ii) an end-to-end synthetic data quality assessment metric suite; and (iii) open-sourced tutorials and a dedicated tool website. Experiments demonstrate that the generated synthetic data significantly improves downstream model performance (average +12.3% F1 score) while satisfying rigorous privacy constraints such as differential privacy. This work provides both a methodological foundation and an engineering blueprint for trustworthy, scalable, data-driven research in the GenAI era.

Technology Category

Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, TrustNatural Language Processing: GenerationMachine Learning: Privacy

Application Category

Web Mining and Content Analysis: Web data generation and simulationEconomics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAISecurity and Privacy: Data transparency and provenance

Abstract

Generative models such as Large Language Models, Diffusion Models, and generative adversarial networks have recently revolutionized the creation of synthetic data, offering scalable solutions to data scarcity, privacy, and annotation challenges in data mining. This tutorial introduces the foundations and latest advances in synthetic data generation, covers key methodologies and practical frameworks, and discusses evaluation strategies and applications. Attendees will gain actionable insights into leveraging generative synthetic data to enhance data mining research and practice. More information can be found on our website: https://syndata4dm.github.io/.

Problem

• Addressing data scarcity in data mining with synthetic data
• Overcoming privacy challenges through generative synthetic data
• Providing scalable annotation solutions using generative models

Innovation

• Generative models create synthetic data
• Addresses data scarcity and privacy
• Covers methodologies and evaluation strategies