The discussion at The Rise of Privacy Tech’s Data Privacy Week 2022 conference addressed various questions about synthetic data, emphasizing the distinctions between synthetic data creation and differential privacy. Synthetic data is generated by algorithms that learn the dataset distribution, maintaining properties like correlations, while differential privacy introduces calibrated noise to obscure individual data points. The conversation highlighted the importance of privacy-protection mechanisms in machine learning to prevent adversarial attacks, with differential privacy being a key method. Synthetic data may not suit studies focusing on outliers or rare populations, as quality can degrade without a large dataset. Effective communication about the potential error in synthetic data is crucial, with its quality being measured based on intended use. Gretel offers synthetic data quality reports to ensure statistical fidelity, and advocates for differentially private querying systems for further understanding original datasets. The discussion concluded with an invitation for continued dialogue within the community.