Package org.deidentifier.arx.metric.v2

Klasse MetricMDNUEntropyPrecomputed

java.lang.Object

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

org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensional

org.deidentifier.arx.metric.v2.MetricMDNUEntropyPrecomputed

Alle implementierten Schnittstellen:

Serializable

Bekannte direkte Unterklassen:

MetricMDNUEntropy, MetricMDNUNMEntropyPrecomputed

public class MetricMDNUEntropyPrecomputed
extends AbstractMetricMultiDimensional

This class provides an efficient implementation of the non-uniform entropy metric. It avoids a cell-by-cell process by utilizing a three-dimensional array that maps identifiers to their frequency for all quasi-identifiers and generalization levels. It further reduces the overhead induced by subsequent calls by caching the results for previous columns and generalization levels. See:
A. De Waal and L. Willenborg: "Information loss through global recoding and local suppression" Netherlands Off Stat, vol. 14, pp. 17–20, 1999.

Siehe auch:

• Serialisierte Form

Verschachtelte Klassen - Übersicht

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

Metric.AggregateFunction

Konstruktorübersicht

Konstruktoren

Konstruktor	Beschreibung
MetricMDNUEntropyPrecomputed(boolean monotonicWithGeneralization, boolean monotonicWithSuppression, boolean independent, double gsFactor, Metric.AggregateFunction function)	Precomputed.

Methodenübersicht

Modifizierer und Typ	Methode	Beschreibung
MetricConfiguration	getConfiguration()	Returns the configuration of this metric.
ILScore	getScore(org.deidentifier.arx.framework.lattice.Transformation<?> node, org.deidentifier.arx.framework.check.groupify.HashGroupify groupify)	Calculates the score.
boolean	isGSFactorSupported()	Returns whether a generalization/suppression factor is supported
boolean	isPrecomputed()	Returns whether the metric is precomputed
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, getGeneralizationFactor, getGeneralizationSuppressionFactor, getInformationLoss, getInformationLoss, getLowerBound, getLowerBound, getName, getSuppressionFactor, initialize, isAbleToHandleClusteredMicroaggregation, isAbleToHandleMicroaggregation, isIndependent, isMonotonic, isMonotonicWithGeneralization, isMonotonicWithSuppression, isMultiDimensional, isWeighted, list

Von Klasse geerbte Methoden java.lang.Object

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

Konstruktordetails

MetricMDNUEntropyPrecomputed

public MetricMDNUEntropyPrecomputed(boolean monotonicWithGeneralization,
 boolean monotonicWithSuppression,
 boolean independent,
 double gsFactor,
 Metric.AggregateFunction function)

Precomputed.

Parameter:

monotonicWithGeneralization -

monotonicWithSuppression -

independent -

gsFactor -

function -

Methodendetails

getConfiguration

public MetricConfiguration getConfiguration()

Returns the configuration of this metric.

Setzt außer Kraft:

getConfiguration 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:

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:

isPrecomputed

public boolean isPrecomputed()

Beschreibung aus Klasse kopiert: Metric

Returns whether the metric is precomputed

Setzt außer Kraft:

isPrecomputed 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: