# Package org.deidentifier.arx.metric.v2

Main package implementing quality models

See: Description

Interface Summary
DomainShare: Base interface for domain shares.

Class Summary
__MetricV2: This internal class provides access to version 2 of all metrics.
AbstractILMultiDimensional: This class implements an information loss which can be represented as a decimal number per quasi-identifier.
AbstractILMultiDimensionalReduced: This class implements an information loss which can be represented as a decimal number per quasi-identifier.
AbstractMetricMultiDimensional: This class provides an abstract skeleton for the implementation of multi-dimensional metrics.
AbstractMetricMultiDimensionalPotentiallyPrecomputed: This class provides an abstract skeleton for the implementation of metrics that can either be precomputed or not.
AbstractMetricSingleDimensional: This class provides an abstract skeleton for the implementation of single-dimensional metrics.
Cardinalities: This class represents cardinalities.
DomainShareInterval<T>: This class represents a set of domain shares for an attribute.
DomainShareMaterialized: This class represents a set of domain shares for an attribute.
DomainShareRedaction: This class represents a set of domain shares for an attribute.
DomainShareReliable: This class represents a reliable set of domain shares for an attribute.
ILMultiDimensionalArithmeticMean: This class implements an information loss which can be represented as a decimal number per quasi-identifier.
ILMultiDimensionalGeometricMean: This class implements an information loss which can be represented as a decimal number per quasi-identifier.
ILMultiDimensionalMax: This class implements an information loss which can be represented as a decimal number per quasi-identifier.
ILMultiDimensionalRank: This class implements an information loss which can be represented as a decimal number per quasi-identifier.
ILMultiDimensionalSum: This class implements an information loss which can be represented as a decimal number per quasi-identifier.
ILMultiDimensionalWithBound: Information loss with a potential lower bound.
ILScore: This class implements information loss using score values for data-independent differential privacy with appropriate comparison semantics (i.e. higher score values are better).
ILSingleDimensional: This class implements an information loss which can be represented as a single decimal number.
ILSingleDimensionalWithBound: Information loss with a potential lower bound.
IO: This class implements serialization for maps
MetricMDHeight: This class provides an implementation of the Height metric.
MetricMDNMLoss: This class implements a variant of the Loss metric.
MetricMDNMLossPotentiallyPrecomputed: This class implements a variant of the Loss metric.
MetricMDNMLossPrecomputed: This class implements a variant of the Loss metric.
MetricMDNMPrecision: This class provides an implementation of a weighted precision metric as proposed in: Sweeney, L. (2002).
MetricMDNUEntropy: This class provides an implementation of the non-uniform entropy metric.
MetricMDNUEntropyPotentiallyPrecomputed: This class provides an implementation of the non-uniform entropy metric.
MetricMDNUEntropyPrecomputed: This class provides an efficient implementation of the non-uniform entropy metric.
MetricMDNUNMEntropy: This class provides an implementation of the non-uniform entropy metric.
MetricMDNUNMEntropyPotentiallyPrecomputed: This class provides an implementation of the non-uniform entropy metric.
MetricMDNUNMEntropyPrecomputed: This class provides an implementation of the non-uniform entropy metric.
MetricMDNUNMNormalizedEntropy: This class provides an implementation of normalized non-uniform entropy.
MetricMDNUNMNormalizedEntropyPotentiallyPrecomputed: This class provides an implementation of normalized non-uniform entropy.
MetricMDNUNMNormalizedEntropyPrecomputed: This class provides an efficient implementation of normalized non-uniform entropy.
MetricMDPrecision: This class provides an implementation of a weighted precision metric as proposed in: Sweeney, L. (2002).
MetricMDStatic: This class provides an implementation of a static metric in which information loss is user-defined per generalization level.
MetricSDAECS: This class provides an implementation of the (normalized) average equivalence class size metric.
MetricSDClassification: This class provides an implementation of the classification metric.
MetricSDDiscernability: This class provides an implementation of the monotonic DM* metric.
MetricSDNMAmbiguity: This class implements a variant of the Ambiguity metric.
MetricSDNMDiscernability: This class provides an implementation of the non-monotonic DM metric.
MetricSDNMEntropyBasedInformationLoss: This class implements a the entropy-based information loss model proposed in: A Game Theoretic Framework for Analyzing Re-Identification Risk.
MetricSDNMKLDivergence: This class implements the KL Divergence metric.
MetricSDNMPublisherPayout: This class implements a model which maximizes publisher benefit according to the model proposed in: A Game Theoretic Framework for Analyzing Re-Identification Risk.
QualityMetadata<T>: A class encapsulating information about data quality
TupleMatcher: A class that supports associating input with output

## Package org.deidentifier.arx.metric.v2 Description

Main package implementing quality models

Author:
  Fabian Prasser