# Package org.deidentifier.arx.criteria

This package implements different variants of class-based privacy criteria, such as k-anonymity, l-diversity, t-closeness and d-presence.

See: Description

Class Summary
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 Summary
EntropyLDiversity.EntropyEstimator: Enumerator of entropy estimators for the entropy-l-diversity privacy model.
KMap.CellSizeEstimator: Estimators for cell sizes in the population.

## Package org.deidentifier.arx.criteria Description

This package implements different variants of class-based privacy criteria, such as k-anonymity, l-diversity, t-closeness and d-presence.
Moreover, this package implements sample-based criteria, such as thresholds on the average re-identification risk, population uniqueness and sample uniqueness.
k-anonymity and d-presence and the sample-based criteria are implicit privacy criteria, i.e., they are implicitly bound to the quasi-identifiers, while the other criteria are explicitly bound to a specific sensitive attribute.

Author:
  Fabian Prasser, Florian Kohlmayer