Privacy
Similarity and disclosure risk are different questions. Read about purpose-limited data handling, controlled evaluation boundaries, and release review for synthetic records. These articles avoid treating random generation or realistic-looking rows as proof that a dataset is safe for unrestricted distribution.
Use this collection to identify the assumptions behind a proposed privacy claim. Consider the source data, recipient, available background information, intermediate artifacts, and intended reuse. A narrow test can provide useful evidence, but it should not be expanded into an unsupported guarantee or a claim of legal compliance.
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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