See: Description
| Class | Description |
|---|---|
| AverageReidentificationRisk |
This criterion ensures that an estimate for the average re-identification risk falls
below a given threshold.
|
| BasicBLikeness |
Basic-beta-Likeness:
Jianneng Cao, Panagiotis Karras: Publishing Microdata with a Robust Privacy Guarantee VLDB 2012. |
| DDisclosurePrivacy |
Delta-disclosure privacy as proposed in:
Justin Brickell and Vitaly Shmatikov: The Cost of Privacy: Destruction of Data-mining Utility in Anonymized Data Publishing Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2008 |
| DistinctLDiversity |
The distinct l-diversity privacy criterion.
|
| DPresence |
The d-presence criterion
Published in:
Nergiz M, Atzori M, Clifton C.
|
| EDDifferentialPrivacy |
(e,d)-Differential Privacy implemented with SafePub as proposed in:
Bild R, Kuhn KA, Prasser F.
|
| EnhancedBLikeness |
Enhanced-beta-Likeness:
Jianneng Cao, Panagiotis Karras: Publishing Microdata with a Robust Privacy Guarantee VLDB 2012. |
| EntropyLDiversity |
The entropy l-diversity privacy model.
|
| EqualDistanceTCloseness |
The t-closeness criterion with equal-distance EMD.
|
| ExplicitPrivacyCriterion |
A privacy criterion that is explicitly bound to a sensitive attribute.
|
| HierarchicalDistanceTCloseness |
The t-closeness criterion with hierarchical-distance EMD.
|
| ImplicitPrivacyCriterion |
A privacy criterion that is implicitly bound to the quasi-identifiers.
|
| Inclusion |
This is a special criterion that does not enforce any privacy guarantees
but allows to define a data subset.
|
| KAnonymity |
The k-anonymity criterion
Published in:
Sweeney L.
|
| KMap |
This class implements the k-map privacy model as proposed by Latanya Sweeney.
|
| LDiversity |
An abstract base class for l-diversity criteria
Published in:
Machanavajjhala A, Kifer D, Gehrke J.
|
| OrderedDistanceTCloseness |
The t-closeness criterion for ordered attributes.
|
| PopulationUniqueness |
This criterion ensures that the population uniqueness falls below a given threshold.
|
| PrivacyCriterion |
An abstract base class for privacy criteria.
|
| ProfitabilityJournalist |
Privacy model for the game theoretic approach proposed in:
A Game Theoretic Framework for Analyzing Re-Identification Risk.
|
| ProfitabilityJournalistNoAttack |
Privacy model for the "no-attack" variant of the game theoretic approach proposed in:
A Game Theoretic Framework for Analyzing Re-Identification Risk.
|
| ProfitabilityProsecutor |
Privacy model for the game theoretic approach proposed in:
A Game Theoretic Framework for Analyzing Re-Identification Risk.
|
| ProfitabilityProsecutorNoAttack |
Privacy model for the "no-attack" variant of the game theoretic approach proposed in:
A Game Theoretic Framework for Analyzing Re-Identification Risk.
|
| RecursiveCLDiversity |
The recursive-(c,l)-diversity criterion.
|
| RiskBasedCriterion |
Abstract class for criteria that ensure that a certain risk measure is lower than or equal to a given threshold
|
| SampleBasedCriterion |
An abstract base class for sample-based privacy criteria.
|
| SampleUniqueness |
This criterion ensures that the sample uniqueness falls below a given threshold.
|
| TCloseness |
An abstract base class for t-closeness criteria as proposed in:
Li N, Li T, Venkatasubramanian S.
|
| Enum | Description |
|---|---|
| EntropyLDiversity.EntropyEstimator |
Enumerator of entropy estimators for the entropy-l-diversity privacy model.
|
| KMap.CellSizeEstimator |
Estimators for cell sizes in the population.
|