Verwendungen von Package
org.deidentifier.arx.metric.v2

Packages, die org.deidentifier.arx.metric.v2 verwenden

Package	Beschreibung
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

Von org.deidentifier.arx verwendete Klassen in org.deidentifier.arx.metric.v2

Klasse	Beschreibung
QualityMetadata	A class encapsulating information about data quality

Von org.deidentifier.arx.metric verwendete Klassen in org.deidentifier.arx.metric.v2

Klasse	Beschreibung
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

Von org.deidentifier.arx.metric.v2 verwendete Klassen in org.deidentifier.arx.metric.v2

Klasse	Beschreibung
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.