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Validate synthetic data quality

Source: R/validate.R
validate_synthetic.Rd

Computes utility and privacy metrics comparing synthetic data to the original real dataset.

Usage

validate_synthetic(
  x,
  metrics = c("distributional", "correlation", "discriminative", "privacy")
)

Arguments

x

A synthetic_data object from synthesize().

metrics

Character vector of metrics:

  • "distributional": KS statistic per numeric variable.

  • "correlation": Frobenius norm of correlation difference.

  • "discriminative": AUC of real-vs-synthetic classifier.

  • "privacy": nearest-neighbor distance ratio.

Value

A synthetic_validation object (tibble) with columns: metric, value, interpretation.

Details

Utility metrics assess how well the synthetic data preserves statistical properties. Privacy metrics assess the risk of re-identification.

Discriminative accuracy near 0.5 means the synthetic data is indistinguishable from real data. Privacy ratio > 1 means synthetic records are not closer to real records than real records are to each other.

References

Snoke J, et al. (2018). General and specific utility measures for synthetic data. Journal of the Royal Statistical Society A, 181(3):663–688. doi:10.1111/rssa.12358

Examples

set.seed(42)
real <- data.frame(age = rnorm(100, 65, 10), sbp = rnorm(100, 130, 20))
syn <- synthesize(real, seed = 42)
validate_synthetic(syn)
#> 
#> ── Synthetic data validation 
#> ks_statistic_mean: 0.085 (Good fidelity)
#> correlation_diff: 0.0263 (Excellent)
#> discriminative_auc: 0.5319 (Indistinguishable)
#> nn_distance_ratio: 0.8892 (Moderate risk)