Package org.deidentifier.arx.metric.v2

Klasse MetricMDStatic

java.lang.Object

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

org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensional

org.deidentifier.arx.metric.v2.MetricMDStatic

Alle implementierten Schnittstellen:

Serializable

public class MetricMDStatic
extends AbstractMetricMultiDimensional

This class provides an implementation of a static metric in which information loss is user-defined per generalization level. This metric will respect attribute weights defined in the configuration. 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	MetricMDStatic(Map<String,List<Double>> infoloss)	Constructor.
protected	MetricMDStatic(Metric.AggregateFunction function, Map<String,List<Double>> infoloss)	Constructor.

Methodenübersicht

Modifizierer und Typ	Methode	Beschreibung
MetricConfiguration	getConfiguration()	Returns the configuration of this metric.
protected ILMultiDimensionalWithBound	getInformationLossInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node, org.deidentifier.arx.framework.check.groupify.HashGroupify g)	Evaluates the metric for the given node.
protected ILMultiDimensionalWithBound	getInformationLossInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node, org.deidentifier.arx.framework.check.groupify.HashGroupifyEntry entry)	Returns the information loss that would be induced by suppressing the given entry.
protected AbstractILMultiDimensional	getLowerBoundInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node)	Returns a lower bound for the information loss for the given node.
protected AbstractILMultiDimensional	getLowerBoundInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node, org.deidentifier.arx.framework.check.groupify.HashGroupify groupify)	Returns a lower bound for the information loss for the given node.
protected void	initializeInternal(org.deidentifier.arx.framework.data.DataManager manager, DataDefinition definition, org.deidentifier.arx.framework.data.Data input, org.deidentifier.arx.framework.data.GeneralizationHierarchy[] hierarchies, ARXConfiguration config)	Implement this to initialize the metric.
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

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, getScore, getSubset, getSuppressionFactor, initialize, isAbleToHandleClusteredMicroaggregation, isAbleToHandleMicroaggregation, isGSFactorSupported, isIndependent, isMonotonic, isMonotonicWithGeneralization, isMonotonicWithSuppression, isMultiDimensional, isPrecomputed, isScoreFunctionSupported, isWeighted, list, round

Von Klasse geerbte Methoden java.lang.Object

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

Konstruktordetails

MetricMDStatic

protected MetricMDStatic(Metric.AggregateFunction function,
 Map<String,List<Double>> infoloss)

Constructor.

Parameter:

function -

infoloss -

MetricMDStatic

protected MetricMDStatic(Map<String,List<Double>> infoloss)

Constructor.

Parameter:

infoloss -

Methodendetails

getConfiguration

public MetricConfiguration getConfiguration()

Returns the configuration of this metric.

Setzt außer Kraft:

getConfiguration 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:

getInformationLossInternal

protected ILMultiDimensionalWithBound getInformationLossInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node,
 org.deidentifier.arx.framework.check.groupify.HashGroupify g)

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

g - The groupify operator of the previous check

Gibt zurück:

the double

getInformationLossInternal

protected ILMultiDimensionalWithBound getInformationLossInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node,
 org.deidentifier.arx.framework.check.groupify.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(org.deidentifier.arx.framework.lattice.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(org.deidentifier.arx.framework.lattice.Transformation<?> node,
 org.deidentifier.arx.framework.check.groupify.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:

initializeInternal

protected void initializeInternal(org.deidentifier.arx.framework.data.DataManager manager,
 DataDefinition definition,
 org.deidentifier.arx.framework.data.Data input,
 org.deidentifier.arx.framework.data.GeneralizationHierarchy[] hierarchies,
 ARXConfiguration config)

Beschreibung aus Klasse kopiert: Metric

Implement this to initialize the metric.

Setzt außer Kraft:

initializeInternal in Klasse AbstractMetricMultiDimensional

Parameter:

manager -

definition -

input -

hierarchies -

config -