Uses of Package
org.deidentifier.arx.metric.v2

Packages that use org.deidentifier.arx.metric.v2

Package	Description
org.deidentifier.arx	This package provides the public API for the ARX anonymization framework.
org.deidentifier.arx.metric	Package providing access to quality models
org.deidentifier.arx.metric.v2	Main package implementing quality models

Classes in org.deidentifier.arx.metric.v2 used by org.deidentifier.arx

Class and Description
QualityMetadata A class encapsulating information about data quality

Classes in org.deidentifier.arx.metric.v2 used by org.deidentifier.arx.metric

Class and Description
AbstractILMultiDimensional This class implements an information loss which can be represented as a decimal number per quasi-identifier.
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.
MetricSDNMEntropyBasedInformationLoss This class implements a the entropy-based information loss model proposed in: A Game Theoretic Framework for Analyzing Re-Identification Risk.
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 A class encapsulating information about data quality

Classes in org.deidentifier.arx.metric.v2 used by org.deidentifier.arx.metric.v2

Class and Description
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.
DomainShare Base interface for domain shares.
DomainShareInterval 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.
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.
MetricMDNMLoss 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).
MetricMDNUEntropyPrecomputed This class provides an efficient implementation of the non-uniform entropy metric.
MetricMDNUNMEntropyPrecomputed This class provides an implementation of the non-uniform entropy metric.
MetricMDNUNMNormalizedEntropyPrecomputed This class provides an efficient implementation of normalized non-uniform entropy.
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.
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.