Package org.deidentifier.arx.metric.v2

Klasse MetricMDNMLoss

java.lang.Object

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

org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensional

org.deidentifier.arx.metric.v2.MetricMDNMLoss

Alle implementierten Schnittstellen:

Serializable

Bekannte direkte Unterklassen:

MetricMDNMLossPrecomputed

public class MetricMDNMLoss
extends AbstractMetricMultiDimensional

This class implements a variant of the Loss metric.

Siehe auch:

• Serialisierte Form

Verschachtelte Klassen - Übersicht

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

Metric.AggregateFunction

Konstruktorübersicht

Konstruktoren

Konstruktor	Beschreibung
MetricMDNMLoss()	Default constructor which treats all transformation methods equally.
MetricMDNMLoss(double gsFactor, Metric.AggregateFunction function)	A constructor that allows to define a factor weighting generalization and suppression.
MetricMDNMLoss(Metric.AggregateFunction function)	Default constructor which treats all transformation methods equally.

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.
String	getName()	Returns the name of metric.
ILScore	getScore(org.deidentifier.arx.framework.lattice.Transformation<?> node, org.deidentifier.arx.framework.check.groupify.HashGroupify groupify)	Calculates the score.
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
boolean	isScoreFunctionSupported()	Returns whether the metric provides a score function
ElementData	render(ARXConfiguration config)	Renders the privacy model
String	toString()	Returns the name of metric.

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

createMaxInformationLoss, createMinInformationLoss, getAggregateFunction

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, getInformationLoss, getInformationLoss, getLowerBound, getLowerBound, initialize, isAbleToHandleClusteredMicroaggregation, isIndependent, isMonotonic, isMonotonicWithGeneralization, isMonotonicWithSuppression, isMultiDimensional, isPrecomputed, isWeighted, list

Von Klasse geerbte Methoden java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Konstruktordetails

MetricMDNMLoss

public MetricMDNMLoss()

Default constructor which treats all transformation methods equally.

MetricMDNMLoss

public MetricMDNMLoss(Metric.AggregateFunction function)

Default constructor which treats all transformation methods equally.

Parameter:

function -

MetricMDNMLoss

public MetricMDNMLoss(double gsFactor,
 Metric.AggregateFunction function)

A constructor that allows to define a factor weighting generalization and suppression.

Parameter:

gsFactor - 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.

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 Metric<AbstractILMultiDimensional>

Gibt zurück:

getGeneralizationSuppressionFactor

public double getGeneralizationSuppressionFactor()

Beschreibung aus Klasse kopiert: Metric

Returns the factor weighting generalization and suppression.

Setzt außer Kraft:

getGeneralizationSuppressionFactor in Klasse Metric<AbstractILMultiDimensional>

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.

getName

public String getName()

Beschreibung aus Klasse kopiert: Metric

Returns the name of metric.

Setzt außer Kraft:

getName in Klasse Metric<AbstractILMultiDimensional>

Gibt zurück:

getScore

public ILScore getScore(org.deidentifier.arx.framework.lattice.Transformation<?> node,
 org.deidentifier.arx.framework.check.groupify.HashGroupify groupify)

Beschreibung aus Klasse kopiert: Metric

Calculates the score. Note: All score functions are expected to return a score value divided by the sensitivity of the score function.

Setzt außer Kraft:

getScore in Klasse Metric<AbstractILMultiDimensional>

Parameter:

node -

groupify -

Gibt zurück:

getSuppressionFactor

public double getSuppressionFactor()

Beschreibung aus Klasse kopiert: Metric

Returns the factor used to weight suppressed values.

Setzt außer Kraft:

getSuppressionFactor in Klasse Metric<AbstractILMultiDimensional>

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:

isScoreFunctionSupported

public boolean isScoreFunctionSupported()

Beschreibung aus Klasse kopiert: Metric

Returns whether the metric provides a score function

Setzt außer Kraft:

isScoreFunctionSupported 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: