Package org.deidentifier.arx.metric.v2

Klasse AbstractMetricMultiDimensionalPotentiallyPrecomputed

java.lang.Object

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

org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensional

org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensionalPotentiallyPrecomputed

Alle implementierten Schnittstellen:

Serializable

Bekannte direkte Unterklassen:

MetricMDNMLossPotentiallyPrecomputed, MetricMDNUEntropyPotentiallyPrecomputed, MetricMDNUNMEntropyPotentiallyPrecomputed, MetricMDNUNMNormalizedEntropyPotentiallyPrecomputed

public abstract class AbstractMetricMultiDimensionalPotentiallyPrecomputed
extends AbstractMetricMultiDimensional

This class provides an abstract skeleton for the implementation of metrics that can either be precomputed or not. The decision is made at runtime depending on data properties.

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

Methodenübersicht

Modifizierer und Typ	Methode	Beschreibung
InformationLoss<?>	createMaxInformationLoss()	Returns an instance of the maximal value.
InformationLoss<?>	createMinInformationLoss()	Returns an instance of the minimal value.
Metric.AggregateFunction	getAggregateFunction()	Returns the aggregate function of a multi-dimensional metric, null otherwise.
protected AbstractMetricMultiDimensional	getDefaultMetric()	Returns the default variant.
double	getGeneralizationFactor()	Returns the factor used weight generalized values.
double	getGeneralizationSuppressionFactor()	Returns the factor weighting generalization and suppression.
protected InformationLossWithBound<AbstractILMultiDimensional>	getInformationLossInternal(Transformation<?> node, HashGroupify groupify)	Evaluates the metric for the given node.
protected InformationLossWithBound<AbstractILMultiDimensional>	getInformationLossInternal(Transformation<?> node, HashGroupifyEntry entry)	Returns the information loss that would be induced by suppressing the given entry.
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 AbstractMetricMultiDimensional	getPrecomputedMetric()	Returns the precomputed variant.
ILScore	getScore(Transformation<?> node, HashGroupify groupify)	Calculates the score.
double	getSuppressionFactor()	Returns the factor used to weight suppressed values.
protected double	getThreshold()	Returns the threshold.
protected void	initializeInternal(DataManager manager, DataDefinition definition, Data input, GeneralizationHierarchy[] ahierarchies, ARXConfiguration config)	Implement this to initialize the metric.
boolean	isIndependent()	Returns whether this metric requires the transformed data or groups to determine information loss.
boolean	isPrecomputed()	Returns whether the metric is precomputed
boolean	isScoreFunctionSupported()	Returns whether the metric provides a score function

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, getConfiguration, getDescription, getDescription, getInformationLoss, getInformationLoss, getLowerBound, getLowerBound, getName, getNumRecords, getSubset, initialize, isAbleToHandleClusteredMicroaggregation, isAbleToHandleMicroaggregation, isGSFactorSupported, isMonotonic, isMonotonicWithGeneralization, isMonotonicWithSuppression, isMultiDimensional, isWeighted, list, render, round, toString

Von Klasse geerbte Methoden java.lang.Object

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

Methodendetails

createMaxInformationLoss

public InformationLoss<?> createMaxInformationLoss()

Beschreibung aus Klasse kopiert: Metric

Returns an instance of the maximal value.

Setzt außer Kraft:

createMaxInformationLoss in Klasse AbstractMetricMultiDimensional

Gibt zurück:

createMinInformationLoss

public InformationLoss<?> createMinInformationLoss()

Beschreibung aus Klasse kopiert: Metric

Returns an instance of the minimal value.

Setzt außer Kraft:

createMinInformationLoss in Klasse AbstractMetricMultiDimensional

Gibt zurück:

getAggregateFunction

public Metric.AggregateFunction getAggregateFunction()

Beschreibung aus Klasse kopiert: Metric

Returns the aggregate function of a multi-dimensional metric, null otherwise.

Setzt außer Kraft:

getAggregateFunction in Klasse AbstractMetricMultiDimensional

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.

getScore

public ILScore getScore(Transformation<?> node,
 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:

isIndependent

public boolean isIndependent()

Beschreibung aus Klasse kopiert: Metric

Returns whether this metric requires the transformed data or groups to determine information loss.

Setzt außer Kraft:

isIndependent 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:

getDefaultMetric

protected AbstractMetricMultiDimensional getDefaultMetric()

Returns the default variant.

Gibt zurück:

getInformationLossInternal

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

Beschreibung aus Klasse kopiert: Metric

Evaluates the metric for the given node.

Angegeben von:

getInformationLossInternal in Klasse Metric<AbstractILMultiDimensional>

Parameter:

node - The node for which to compute the information loss

groupify - The groupify operator of the previous check

Gibt zurück:

the double

getInformationLossInternal

protected InformationLossWithBound<AbstractILMultiDimensional> getInformationLossInternal(Transformation<?> node,
 HashGroupifyEntry entry)

Beschreibung aus Klasse kopiert: Metric

Returns the information loss that would be induced by suppressing the given entry. The loss is not necessarily consistent with the loss that is computed by getInformationLoss(node, groupify) but is guaranteed to be comparable for different entries from the same groupify operator.

Angegeben von:

getInformationLossInternal in Klasse Metric<AbstractILMultiDimensional>

Parameter:

entry -

Gibt zurück:

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.

Angegeben von:

getLowerBoundInternal in Klasse Metric<AbstractILMultiDimensional>

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!

Angegeben von:

getLowerBoundInternal in Klasse Metric<AbstractILMultiDimensional>

Parameter:

node -

groupify -

Gibt zurück:

getPrecomputedMetric

protected AbstractMetricMultiDimensional getPrecomputedMetric()

Returns the precomputed variant.

Gibt zurück:

getThreshold

protected double getThreshold()

Returns the threshold.

Gibt zurück:

initializeInternal

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

Beschreibung aus Klasse kopiert: Metric

Implement this to initialize the metric.

Setzt außer Kraft:

initializeInternal in Klasse AbstractMetricMultiDimensional

Parameter:

manager -

definition -

input -

ahierarchies -

config -