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Audit a masque object for leakage and shareability risks

Source: R/audit.R
audit_mask.Rd

Returns a per-column audit tibble and prints a severity-grouped report. Auto-runs in mode = "collaborate" at mask() time and stores the result on the masque object (m@audit); for local-mode audits or explicit re-audits, pass the original data frame via original.

Usage

audit_mask(m, original = NULL, print = TRUE)

Arguments

m

A masque object from mask().

original

Optional. Required when m@audit is NULL (typically in local mode). Used to recompute exact-match-pct etc. on demand.

print

Logical; if TRUE (default), print a styled report.

Value

The audit tibble, returned invisibly.

Details

Each row of the returned tibble holds:

  • col: column name in the original.

  • role: assigned role.

  • kind: storage kind.

  • leakage_class: low, medium, or high.

  • n_unique_levels: distinct non-NA values (categorical only).

  • freq_min: minimum per-level frequency (categorical only).

  • exact_match_pct: percentage of synthetic cells equal to the original cell (numeric only; cell-by-cell).

  • na_pct: percentage of NA cells in the original column.

  • na_pattern_uniqueness: fraction of rows in the original with a globally unique NA pattern (one number per data frame, repeated on every row).

  • alias_status: aliased, passthrough, or dropped.

  • notes: short human summary.

Classification heuristics (CODEX-aligned):

  • Retained PII-pattern column -> high.

  • Treatment unaliased in collaborate -> high.

  • Categorical covariate with a frequency-1 level in collaborate -> high.

  • Outcome with exact-match-pct > 1\

  • Numeric covariate with exact-match-pct > 5\

  • Ignore column retained in local -> low (informational).

Step 7 will lower numeric exact-match-pct under collaborate by adding within-resolution jitter; until then, expect medium leakage on collaborate-mode numerics.

See also

mask().

Examples

r <- propose_roles(iris)
r$role[r$col == "Sepal.Length"] <- "outcome"
r$role[r$col == "Species"]      <- "covariate"
m <- mask(iris, r, mode = "collaborate", seed = 1)
audit_mask(m)
#> 
#> ── masque audit (mode = collaborate) ───────────────────────────────────────────
#> • 0 HIGH, 0 medium, 5 low across 5 columns
#> • Rows with a globally unique NA pattern: 0.0%
#> 
#> ── LOW (5) ──
#> 
#> ℹ   outcome   Sepal.Length                      ok
#> ℹ   covariate Sepal.Width                       ok
#> ℹ   covariate Petal.Length                      ok
#> ℹ   covariate Petal.Width                       ok
#> ℹ   covariate Species                           levels aliased