# org.deidentifier.arx.criteria (ARX GUI Documentation)

# 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
  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 (k,b)-SDGS as proposed in: Ninghui Li, Wahbeh H.
  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. As an alternative to explicitly providing data about the underlying population, cell sizes can be can be estimated with the D3 (Poisson) and D4 (zero-truncated Poisson) estimators proposed in: K.
  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
  Enum | Description
  --- | ---
  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.