The field of privacy-preserving data generation and verification is experiencing significant growth, with a focus on developing innovative methods for protecting sensitive information while maintaining data utility. Recent research has explored the use of generative models, differential privacy, and flow matching techniques to synthesize data that preserves privacy guarantees. These advances have the potential to unlock the value of previously inaccessible datasets and replace traditional anonymization methods. Noteworthy papers in this area have demonstrated the effectiveness of privacy-preserving generative models in clinical settings, the promise of flow matching for tabular data synthesis, and the development of practical guides for generating synthetic data with differential privacy. Additionally, researchers have made progress in designing efficient verification methods for private machine learning models, enabling data providers to trust that their data is being used in a privacy-preserving manner. Notable papers include: Privacy-Preserving Generative Modeling and Clinical Validation of Longitudinal Health Records for Chronic Disease, which enhanced a state-of-the-art time-series generative model to handle longitudinal clinical data while incorporating quantifiable privacy safeguards. Flow Matching for Tabular Data Synthesis, which presented a comprehensive empirical study comparing flow matching with state-of-the-art diffusion methods in tabular data synthesis. How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy, which explored the full suite of techniques surrounding differentially private synthetic data and outlined the components needed in a system that generates such data.