Package org.deidentifier.arx.metric.v2

Klasse MetricMDNUNMEntropyPrecomputed

java.lang.Object

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

org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensional

org.deidentifier.arx.metric.v2.MetricMDNUEntropyPrecomputed

org.deidentifier.arx.metric.v2.MetricMDNUNMEntropyPrecomputed

Alle implementierten Schnittstellen:

Serializable

Bekannte direkte Unterklassen:

MetricMDNUNMEntropy, MetricMDNUNMNormalizedEntropyPrecomputed

public class MetricMDNUNMEntropyPrecomputed
extends MetricMDNUEntropyPrecomputed

This class provides an implementation of the non-uniform entropy metric. 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

Feldübersicht

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

k

Konstruktorübersicht

Konstruktoren

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

Methodenübersicht

Modifizierer und Typ	Methode	Beschreibung
MetricConfiguration	getConfiguration()	Returns the configuration of this metric.
protected ILMultiDimensionalWithBound	getInformationLossInternal(Transformation<?> node, HashGroupify g)	Evaluates the metric for the given node.
protected AbstractILMultiDimensional	getLowerBoundInternal(Transformation<?> node)	Returns a lower bound for the information loss for the given node.
protected AbstractILMultiDimensional	getLowerBoundInternal(Transformation<?> node, HashGroupify groupify)	Returns a lower bound for the information loss for the given node.
protected void	initializeInternal(DataManager manager, DataDefinition definition, Data input, GeneralizationHierarchy[] hierarchies, ARXConfiguration config)	Implement this to initialize the metric.
boolean	isGSFactorSupported()	Returns whether a generalization/suppression factor is supported
boolean	isPrecomputed()	Returns whether the metric is precomputed
ElementData	render(ARXConfiguration config)	Renders the privacy model
String	toString()	Returns the name of metric.

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

getInformationLossInternal, getInformationLossInternalRaw, getScore, getUpperBounds, initialize, isScoreFunctionSupported

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

createInformationLoss, createMaxInformationLoss, createMinInformationLoss, getAggregateFunction, 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, getGeneralizationFactor, getGeneralizationSuppressionFactor, getInformationLoss, getInformationLoss, getLowerBound, getLowerBound, getName, getNumRecords, getSubset, getSuppressionFactor, initialize, isAbleToHandleClusteredMicroaggregation, isAbleToHandleMicroaggregation, isIndependent, 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

MetricMDNUNMEntropyPrecomputed

protected MetricMDNUNMEntropyPrecomputed()

Creates a new instance.

MetricMDNUNMEntropyPrecomputed

protected MetricMDNUNMEntropyPrecomputed(double gsFactor,
 Metric.AggregateFunction function)

Creates a new instance.

Parameter:

gsFactor -

function -

Methodendetails

getConfiguration

public MetricConfiguration getConfiguration()

Returns the configuration of this metric.

Setzt außer Kraft:

getConfiguration in Klasse MetricMDNUEntropyPrecomputed

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 MetricMDNUEntropyPrecomputed

Gibt zurück:

isPrecomputed

public boolean isPrecomputed()

Beschreibung aus Klasse kopiert: Metric

Returns whether the metric is precomputed

Setzt außer Kraft:

isPrecomputed in Klasse MetricMDNUEntropyPrecomputed

Gibt zurück:

render

public ElementData render(ARXConfiguration config)

Beschreibung aus Klasse kopiert: Metric

Renders the privacy model

Setzt außer Kraft:

render in Klasse MetricMDNUEntropyPrecomputed

Gibt zurück:

toString

public String toString()

Beschreibung aus Klasse kopiert: Metric

Returns the name of metric.

Setzt außer Kraft:

toString in Klasse MetricMDNUEntropyPrecomputed

Gibt zurück:

getInformationLossInternal

protected ILMultiDimensionalWithBound getInformationLossInternal(Transformation<?> node,
 HashGroupify g)

Beschreibung aus Klasse kopiert: Metric

Evaluates the metric for the given node.

Setzt außer Kraft:

getInformationLossInternal in Klasse MetricMDNUEntropyPrecomputed

Parameter:

node - The node for which to compute the information loss

g - The groupify operator of the previous check

Gibt zurück:

the double

getLowerBoundInternal

protected AbstractILMultiDimensional getLowerBoundInternal(Transformation<?> node)

Beschreibung aus Klasse kopiert: Metric

Returns a lower bound for the information loss for the given node. This can be used to expose the results of monotonic shares of a metric, which can significantly speed-up the anonymization process. If no such metric exists, simply return null.

Setzt außer Kraft:

getLowerBoundInternal in Klasse MetricMDNUEntropyPrecomputed

Parameter:

node -

Gibt zurück:

getLowerBoundInternal

protected AbstractILMultiDimensional getLowerBoundInternal(Transformation<?> node,
 HashGroupify groupify)

Beschreibung aus Klasse kopiert: Metric

Returns a lower bound for the information loss for the given node. This can be used to expose the results of monotonic shares of a metric, which can significantly speed-up the anonymization process. If no such metric exists, simply return null.

This variant of the method allows computing a monotonic share based on a groupified data representation. IMPORTANT NOTE: The groups may not have been classified correctly when the method is called, i.e., HashGroupifyEntry.isNotOutlier may not be set correctly!

Setzt außer Kraft:

getLowerBoundInternal in Klasse MetricMDNUEntropyPrecomputed

Parameter:

node -

groupify -

Gibt zurück:

initializeInternal

protected void initializeInternal(DataManager manager,
 DataDefinition definition,
 Data input,
 GeneralizationHierarchy[] hierarchies,
 ARXConfiguration config)

Beschreibung aus Klasse kopiert: Metric

Implement this to initialize the metric.

Setzt außer Kraft:

initializeInternal in Klasse MetricMDNUEntropyPrecomputed

Parameter:

manager -

definition -

input -

hierarchies -

config -