Comprehensive Review of Synthetic Data Generation Techniques and Their Applications in Healthcare, Finance, and Marketing

Abstract

The demand for privacy-preserving, high-quality data has driven the rapid development of synthetic data generation techniques. Data scarcity, privacy regulations, and the need for large-scale datasets are some of the challenges these methods aim to address. Key methodologies for synthetic data generation include Generative Adversarial Networks (GANs), Variational Autoen- coders (VAEs), and rule-based systems. This review highlights the strengths, limitations, and practical applications of these techniques across various fields. It also explores ethical considerations related to privacy and fairness, focusing on privacy-preserving models such as differential privacy and federated learning. Despite the potential of synthetic data to overcome major barriers in data-driven industries, issues around data fidelity, fairness, and utility remain unresolved. Future research should prioritize the responsible use of synthetic data.

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