ksb.csle.didentification.verification.check
The probabilistic L-diversity method checks the probability of the most frequent attribute values in the equivalence class to be less than 1/l.
The probabilistic L-diversity method checks the probability of the most frequent attribute values in the equivalence class to be less than 1/l. The table is said to be probabilistic l-diverse if every equivalence satisfy the probabilistic l-diverse.
Boolean return true when satisfying privacy policy
The common L-diversity method just checks whether the number of sensitive attributes is greater than given L-constraints.
The common L-diversity method just checks whether the number of sensitive attributes is greater than given L-constraints. Generally, an equivalence class is l-diverse if contains at least 'l' well-represented values for the sensitive attribute. A table is l-diverse if every equivalence is l-diverse
The anonymized dataframe
Double return the l-diversity value
Checks the anonymized dataframe satisfies the given l-Diversity constraint.
Checks the anonymized dataframe satisfies the given l-Diversity constraint.
The anonymized dataframe
the given l-diversity constraint
Boolean return true if satisfying the given l-diversity constraint
This class checks whether the anonymized data satisfies the probabilistic L-diversity constraint. Note that this class checks the l-diversity value in an single equivalence class, not the dataframe.