Editing Synthetic Data (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Synthetic Data Approach Tradeoffs
! Method !! Fidelity !! Privacy !! Utility || Complexity
|-
| Rule-based simulation || Medium || High || High (domain-specific) || High
|-
| Statistical sampling || Low-medium || High || Low-medium || Low
|-
| CTGAN/TVAE || High || Medium || High || Medium
|-
| Diffusion model || Very high || Low || Very high || High
|-
| DP-GAN || Medium || Guaranteed (ε,δ) || Lower || High
|}

'''Failure modes''': Mode collapse in GAN synthesis — the generator produces limited variety. Privacy leakage — even without direct record memorization, synthetic data can allow membership inference. Distribution mismatch — synthetic data looks similar in aggregate but fails on edge cases the model learned from real data. Overfitting to synthetic data — model learns synthetic-data-specific artifacts.
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