Privacy-Preserving Synthetic Data

Privacy-preserving synthetic data is a specialized approach that tries to balance analytical value with privacy protection. The goal is to generate data that is abstract enough to avoid re-identifying real individuals while still being useful for modeling. This becomes especially important in healthcare, finance, and public-sector data. However, being synthetic does not automatically mean being safe; risks such as membership inference and near-copy leakage must also be tested. For that reason, privacy-preserving synthetic data must be validated both in terms of generation quality and attack resistance.