L-Diversity
Technical
An extension of k-anonymity that requires each equivalence class to contain at least l well-represented values for any sensitive attribute, defending against attribute disclosure.
See also
- K-AnonymityA privacy model that ensures each record in a release is indistinguishable from at least k-1 others on quasi-identifiers, reducing re-identification risk.
- T-ClosenessAn extension of k-anonymity and l-diversity that requires the distribution of a sensitive attribute in any equivalence class to be close to the distribution in the overall table, defending against skewness and similarity attacks.
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