Beyond Augmentation Performance: A Utility, Attribution, and Privacy Audit for Synthetic Clinical Spectra

Generative models for small clinical datasets are commonly evaluated by adding synthetic samples to real training data and measuring downstream performance. This mixed-training benchmark can confound generator quality with class rebalancing, does not isolate the contribution of generated samples, and provides no evidence of patient-level privacy. We address these limitations with a structured empirical audit that evaluates three distinct properties: utility through train-on-synthetic, test-on-real substitution under prespecified equivalence testing; attribution through reverse transfer and zero-signal controls; and privacy through membership-inference and copy-rate gates. On ATR-FTIR, a latent-diffusion generator with near-chance synthetic-to-real transfer matches its corrected counterpart in augmentation AUC, showing that mixed-training performance can conceal generator failure. Across ATR-FTIR (\(n=584\)) and LIBS (\(n=112\)), linear PCA-Gaussian matches or exceeds the individual nonlinear generators, while mixtures improve substitution stability. Post-hoc corrections create apparent utility even from label-shuffled or noise inputs, whereas SMOTE shows strong utility yet suffers from near-perfect membership discrimination under the evaluated distance-based attack. In the end we produce fully synthetic artifacts with a classifier passing equivalence criteria with a one trained on real data. However, a post-hoc full-spectrum attack detects membership signal in the assembled LIBS artifact (\(AUC=0.58\), \(p=0.004\)), showing that empirical privacy depends on the adversarial representation. Thus, augmentation performance alone does not establish generator utility; utility, attribution, and privacy require separate evaluation. Our audit is a falsification framework, not a formal privacy guarantee, clinical validation, or certification of release safety.