Attribute disclosure risk — attribute_disclosure

Estimates the fraction of synthetic rows where a sensitive column value can be correctly inferred from known columns via a k-NN lookup in the real training data.

Usage

attribute_disclosure_risk(real, synthetic, sensitive_col, known_cols, k = 1L)

Arguments

real, synthetic: Data frames.
sensitive_col: Name of the column to protect.
known_cols: Character vector of numeric columns assumed known to an adversary. Categorical columns are rejected with a clear error.
k: Number of nearest neighbors used in inference.

Value

A scalar in [0, 1]; lower = more private.

Details

known_cols must be numeric, because nearest-neighbour lookup operates on Euclidean distance over the columns. If you want to use a categorical column as a known attribute, one-hot encode it first (e.g. with model.matrix(~ col - 1, data)).

Examples

real <- data.frame(age = sample(20:60, 50, replace = TRUE),
                   income = sample(c("low", "high"), 50, replace = TRUE),
                   stringsAsFactors = FALSE)
syn  <- real[sample(50), ]
attribute_disclosure_risk(real, syn, sensitive_col = "income", known_cols = "age")
#> [1] 0.72