Synthetic data
Useful records need more than realism. These guides examine generated tables, structured healthcare records, and defensive fraud experiments. Each starts with a defined purpose and keeps schema validity, statistical resemblance, downstream utility, and privacy as separate evaluation questions.
Begin with the general tabular workflow, then follow the domain-specific guide that matches your dataset. Keep the evaluation boundary intact, document the sampling process, and choose a release context supported by the evidence. Synthetic records should never be silently presented as newly observed people or events.
GANs for Fraud Detection: An Experiment Without Data Leakage
Design a defensive augmentation experiment with time-aware splits, fair baselines, and thresholds tied to a real review workflow.
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Evaluating CTGAN: More Than a Realistic-Looking Table
Check validity, relationships, downstream utility, and disclosure risk before accepting a synthetic dataset.
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Synthetic Healthcare Data: Validate Utility and Privacy Separately
A purpose-limited workflow for synthetic patient records, covering schema design, utility testing, privacy review, and release decisions.
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