Published Paper Details:

Across several recent studies, diffusion models [10] have become leading approaches for generating high-quality synthetic data, especially for complex, limited, or sensitive datasets. These works emphasize the increasing demand for effective data generation and imputation methods in areas such as healthcare, finance, and rare-event detection, where real-world data is often scarce, imbalanced, or constrained by privacy concerns. Diffusion-based frameworks [11][15]--including TabDDPM [17], transformer-based architectures, and privacy-preserving federated diffusion systems [25]--have shown significant advantages over conventional GAN and VAE models [2], providing more stable training, higher-quality sample generation, and better support for mixed-type tabular data. These models are capable of handling tasks such as Rare- event augmentation, missing data imputation. and privacy preservation. Compliant financial data synthesis, and pandemic related medical image generation [8] Surveys and systematic reviews further emphasize diffusion models' growing role in addressing challenges like class imbalance, multimodal feature distributions, missing values, and privacy risks. Overall, the combined body of work shows that diffusion models are reshaping synthetic data generation across modalities--enabling more accurate, fair, and privacy-safe machine learning in situations where data is scarce, sensitive, or difficult to obtain.