Package org.deidentifier.arx.metric.v2

Klasse MetricMDNMLossPotentiallyPrecomputed

java.lang.Object

org.deidentifier.arx.metric.Metric<AbstractILMultiDimensional>

org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensional

org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensionalPotentiallyPrecomputed

org.deidentifier.arx.metric.v2.MetricMDNMLossPotentiallyPrecomputed

Alle implementierten Schnittstellen:

Serializable

public class MetricMDNMLossPotentiallyPrecomputed
extends AbstractMetricMultiDimensionalPotentiallyPrecomputed

This class implements a variant of the Loss metric. TODO: Add reference.

Siehe auch:

• Serialisierte Form

Verschachtelte Klassen - Übersicht

Von Klasse geerbte verschachtelte Klassen/Schnittstellen org.deidentifier.arx.metric.Metric

Metric.AggregateFunction

Feldübersicht

Von Klasse geerbte Felder org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensional

k

Konstruktorübersicht

Konstruktoren

Modifizierer	Konstruktor	Beschreibung
protected	MetricMDNMLossPotentiallyPrecomputed(double threshold)	Creates a new instance.
protected	MetricMDNMLossPotentiallyPrecomputed(double threshold, double gsFactor, Metric.AggregateFunction function)	Creates a new instance.
protected	MetricMDNMLossPotentiallyPrecomputed(double threshold, Metric.AggregateFunction function)	Creates a new instance.

Methodenübersicht

Modifizierer und Typ	Methode	Beschreibung
MetricConfiguration	getConfiguration()	Returns the configuration of this metric.
double	getGeneralizationFactor()	Returns the factor used weight generalized values.
double	getGeneralizationSuppressionFactor()	Returns the factor weighting generalization and suppression.
double	getSuppressionFactor()	Returns the factor used to weight suppressed values.
boolean	isAbleToHandleMicroaggregation()	Returns whether this metric handles microaggregation
boolean	isGSFactorSupported()	Returns whether a generalization/suppression factor is supported
ElementData	render(ARXConfiguration config)	Renders the privacy model
String	toString()	Returns the name of metric.

Von Klasse geerbte Methoden org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensionalPotentiallyPrecomputed

createMaxInformationLoss, createMinInformationLoss, getAggregateFunction, getDefaultMetric, getInformationLossInternal, getInformationLossInternal, getLowerBoundInternal, getLowerBoundInternal, getPrecomputedMetric, getScore, getThreshold, initializeInternal, isIndependent, isPrecomputed, isScoreFunctionSupported

Von Klasse geerbte Methoden org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensional

createInformationLoss, getAggregationFunctionsGeneralized, getAggregationFunctionsNonGeneralized, getAggregationIndicesGeneralized, getAggregationIndicesNonGeneralized, getAggregationInformation, getDimensions, getDimensionsAggregated, getDimensionsGeneralized, initialize, setMax, setMin

Von Klasse geerbte Methoden org.deidentifier.arx.metric.Metric

createAECSMetric, createAECSMetric, createAmbiguityMetric, createClassificationMetric, createClassificationMetric, createDiscernabilityMetric, createDiscernabilityMetric, createEntropyBasedInformationLossMetric, createEntropyBasedInformationLossMetric, createEntropyMetric, createEntropyMetric, createEntropyMetric, createEntropyMetric, createEntropyMetric, createEntropyMetric, createHeightMetric, createHeightMetric, createInstanceOfHighestScore, createInstanceOfLowestScore, createKLDivergenceMetric, createLossMetric, createLossMetric, createLossMetric, createLossMetric, createMetric, createNormalizedEntropyMetric, createNormalizedEntropyMetric, createPrecisionMetric, createPrecisionMetric, createPrecisionMetric, createPrecisionMetric, createPrecisionMetric, createPrecisionMetric, createPrecisionMetric, createPrecisionMetric, createPrecomputedEntropyMetric, createPrecomputedEntropyMetric, createPrecomputedEntropyMetric, createPrecomputedEntropyMetric, createPrecomputedEntropyMetric, createPrecomputedEntropyMetric, createPrecomputedLossMetric, createPrecomputedLossMetric, createPrecomputedLossMetric, createPrecomputedLossMetric, createPrecomputedNormalizedEntropyMetric, createPrecomputedNormalizedEntropyMetric, createPublisherPayoutMetric, createPublisherPayoutMetric, createStaticMetric, createStaticMetric, getDescription, getDescription, getInformationLoss, getInformationLoss, getLowerBound, getLowerBound, getName, getNumRecords, getSubset, initialize, isAbleToHandleClusteredMicroaggregation, isMonotonic, isMonotonicWithGeneralization, isMonotonicWithSuppression, isMultiDimensional, isWeighted, list, round

Von Klasse geerbte Methoden java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Konstruktordetails

MetricMDNMLossPotentiallyPrecomputed

protected MetricMDNMLossPotentiallyPrecomputed(double threshold)

Creates a new instance. The precomputed variant will be used if #distinctValues / #rows Ungültige Eingabe: "<"= threshold for all quasi-identifiers.

Parameter:

threshold -

MetricMDNMLossPotentiallyPrecomputed

protected MetricMDNMLossPotentiallyPrecomputed(double threshold,
 Metric.AggregateFunction function)

Creates a new instance. The precomputed variant will be used if #distinctValues / #rows Ungültige Eingabe: "<"= threshold for all quasi-identifiers.

Parameter:

threshold -

function -

MetricMDNMLossPotentiallyPrecomputed

protected MetricMDNMLossPotentiallyPrecomputed(double threshold,
 double gsFactor,
 Metric.AggregateFunction function)

Creates a new instance. The precomputed variant will be used if #distinctValues / #rows Ungültige Eingabe: "<"= threshold for all quasi-identifiers.

Parameter:

threshold -

gsFactor -

function -

Methodendetails

getConfiguration

public MetricConfiguration getConfiguration()

Returns the configuration of this metric.

Setzt außer Kraft:

getConfiguration in Klasse Metric<AbstractILMultiDimensional>

Gibt zurück:

getGeneralizationFactor

public double getGeneralizationFactor()

Beschreibung aus Klasse kopiert: Metric

Returns the factor used weight generalized values.

Setzt außer Kraft:

getGeneralizationFactor in Klasse AbstractMetricMultiDimensionalPotentiallyPrecomputed

Gibt zurück:

getGeneralizationSuppressionFactor

public double getGeneralizationSuppressionFactor()

Beschreibung aus Klasse kopiert: Metric

Returns the factor weighting generalization and suppression.

Setzt außer Kraft:

getGeneralizationSuppressionFactor in Klasse AbstractMetricMultiDimensionalPotentiallyPrecomputed

Gibt zurück:

A factor [0,1] weighting generalization and suppression. The default value is 0.5, which means that generalization and suppression will be treated equally. A factor of 0 will favor suppression, and a factor of 1 will favor generalization. The values in between can be used for balancing both methods.

getSuppressionFactor

public double getSuppressionFactor()

Beschreibung aus Klasse kopiert: Metric

Returns the factor used to weight suppressed values.

Setzt außer Kraft:

getSuppressionFactor in Klasse AbstractMetricMultiDimensionalPotentiallyPrecomputed

Gibt zurück:

isAbleToHandleMicroaggregation

public boolean isAbleToHandleMicroaggregation()

Beschreibung aus Klasse kopiert: Metric

Returns whether this metric handles microaggregation

Setzt außer Kraft:

isAbleToHandleMicroaggregation in Klasse Metric<AbstractILMultiDimensional>

Gibt zurück:

isGSFactorSupported

public boolean isGSFactorSupported()

Beschreibung aus Klasse kopiert: Metric

Returns whether a generalization/suppression factor is supported

Setzt außer Kraft:

isGSFactorSupported in Klasse Metric<AbstractILMultiDimensional>

Gibt zurück:

render

public ElementData render(ARXConfiguration config)

Beschreibung aus Klasse kopiert: Metric

Renders the privacy model

Angegeben von:

render in Klasse Metric<AbstractILMultiDimensional>

Gibt zurück:

toString

public String toString()

Beschreibung aus Klasse kopiert: Metric

Returns the name of metric.

Setzt außer Kraft:

toString in Klasse Metric<AbstractILMultiDimensional>

Gibt zurück: