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Type 2 diabetes affects over 400 million people worldwide and requires lifelong management through continuous monitoring of laboratory values, medications, and comorbidities, yet the use of longitudinal electronic health records for research is restricted by privacy regulations such as HIPAA and GDPR, making synthetic data generation an important alternative for preserving utility while protecting confidentiality. However, existing synthetic data models often fail to accurately capture temporal treatment effects and the gradual development of comorbidities, limiting their usefulness for downstream clinical and machine learning applications. To address this, a time-series generative adversarial network is proposed for longitudinal diabetes data, incorporating a temporal encoder for irregular sampling, a treatment-conditioned generator, and dual discriminators that evaluate both static patient characteristics and dynamic clinical trajectories to ensure consistency between interventions and outcomes. By explicitly modeling temporal dependencies and comorbidity structures, the framework produces more realistic synthetic patient records that better reflect disease progression and medication-response relationships, thereby enabling privacy-preserving data sharing while supporting robust secondary analyses and future applications in chronic disease modeling.

Rare pediatric tumors like sarcomas, neuroblastoma, medulloblastoma, and retinoblastoma pose a challenge for developing deep learning models due to the limited availability of histopathology images, which are distributed across multiple institutions. This scarcity is compounded by privacy concerns, as whole-slide images often contain sensitive clinical and genomic data, and generative adversarial networks (GANs) risk memorizing and leaking training samples. To address this, a differentially private GAN framework is proposed for synthesizing high-resolution histopathology patches of rare pediatric cancers. The framework incorporates a generator for image synthesis, a discriminator for realism assessment, per-sample gradient clipping, Gaussian noise injection, and a privacy accountant, ensuring provable privacy guarantees during the training process. The synthetic images generated can aid in data augmentation, model pre-training, and benchmarking without exposing identifiable pathology data, offering a privacy-preserving solution for dataset augmentation while emphasizing the importance of clinical validation.