l-diversity verification

Checks whether a data.frame satisfies l-diversity: within each equivalence class defined by the quasi-identifiers, the sensitive attribute must take at least l distinct values.

Usage

ldivvrf(data, qids, sensitive, l = 3)

Arguments

data

data.frame.

qids

Character vector of QI column names.

sensitive

Name of the sensitive-attribute column.

l

Minimum number of distinct sensitive values per class. Default 3.

Value

A list with class "morie_l_div" containing:

satisfies

logical.

l

the threshold used.

min_diversity

integer, lowest per-class distinct count.

n_classes

integer.

n_violations

integer, classes below the threshold.

violating_classes

data.frame of class keys plus their .diversity count.

summary

human-readable.

Details

Only known (non-NA) sensitive values count towards diversity: a class whose rows read "HIV", NA, NA tells an adversary the attribute is HIV or withheld, which is diversity 1. Classes are formed as in kanonvrf(), with NA in a quasi-identifier as a level of its own, and sensitive values are compared exactly in the same way (a factor level that is itself NA is withheld, not a value).

Examples

df <- data.frame(
  age = c(25, 25, 25, 25, 32, 32, 32),
  sex = c("F", "F", "F", "F", "M", "M", "M"),
  dx  = c("A", "B", "C", "A", "X", "Y", "Z")
)

# Class {25,F} has 3 distinct dx (A,B,C); {32,M} has 3 (X,Y,Z) -> l=3 holds.
res <- ldivvrf(df, c("age", "sex"), "dx", l = 3)
res$summary
#> [1] "l=3: SATISFIED (min diversity=3; 0/2 classes below threshold)"
res$satisfies
#> [1] TRUE
res$min_diversity
#> [1] 3

# Demanding l = 4 fails: no class has 4 distinct sensitive values.
bad <- ldivvrf(df, c("age", "sex"), "dx", l = 4)
bad$satisfies
#> [1] FALSE
bad$violating_classes
#>   age sex .diversity
#> 1  25   F          3
#> 2  32   M          3

# k-anonymity and l-diversity are complementary: check both.
kanonvrf(df, c("age", "sex"), k = 3)$satisfies
#> [1] TRUE