Compute privacy risk metrics — privacy

Evaluates re-identification risk of synthetic data through multiple privacy metrics: nearest-neighbor distance ratio, membership inference accuracy, and attribute disclosure risk.

Usage

privacy_risk(x, sensitive_cols = NULL)

Arguments

x: A synthetic_data object from synthesize.
sensitive_cols: Character vector (optional). Columns considered sensitive for attribute disclosure assessment.

Value

A privacy_assessment object (tibble) with columns: metric, value, risk_level.

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)
privacy_risk(syn)
#> 
#> ── Privacy risk assessment 
#> [!] nn_distance_ratio: 0.8892 (Medium)
#> [OK] membership_inference_acc: 0.535 (Low)