The common L-diversity method just checks whether the number of sensitive attributes in single equivalence class is greater than given L-constraints.
The common L-diversity method just checks whether the number of sensitive attributes in single equivalence class is greater than given L-constraints.
Double return the l-diversity value
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 is a base class to check whether the anonymized data satisfies the L-diversity constraints. Note that there are some variants of L-diversity. Currently, common l-diversity, entropy l-diversity, probabilistic l-diversity, and recursive C-L diversity are implemented.