# Uses of Package org.deidentifier.arx.criteria (ARX API Documentation)

# Uses of Package
org.deidentifier.arx.criteria

- Packages that use org.deidentifier.arx.criteria
  Package | Description
  --- | ---
  org.deidentifier.arx | This package provides the public API for the ARX anonymization framework.
  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.

- Classes in org.deidentifier.arx.criteria used by org.deidentifier.arx
  Class and Description
  PrivacyCriterion An abstract base class for privacy criteria.
  SampleBasedCriterion An abstract base class for sample-based privacy criteria.

- Classes in org.deidentifier.arx.criteria used by org.deidentifier.arx.criteria
  Class and 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.
  EntropyLDiversity.EntropyEstimator Enumerator of entropy estimators for 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.
  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.
  KMap.CellSizeEstimator Estimators for cell sizes in the population.
  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.
  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.