Generate privacy-safe, statistically faithful synthetic data with the right method and rigorous quality validation.
## CONTEXT Synthetic data has become a practical answer to privacy constraints, class imbalance, and data scarcity in 2026, but bad synthetic data is worse than none: it can leak private records, distort correlations, or train models that fail on real data. The method must match the goal, from simple SMOTE-style…
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