Package org.deidentifier.arx.metric.v2
Klasse MetricSDNMKLDivergence
java.lang.Object
org.deidentifier.arx.metric.Metric<ILSingleDimensional>
org.deidentifier.arx.metric.v2.AbstractMetricSingleDimensional
org.deidentifier.arx.metric.v2.MetricSDNMKLDivergence
- Alle implementierten Schnittstellen:
Serializable
This class implements the KL Divergence metric.
Ashwin Machanavajjhala, Daniel Kifer, Johannes Gehrke, Muthuramakrishnan Venkitasubramaniam:
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 1 Issue 1, March 2007
- Siehe auch:
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Verschachtelte Klassen - Übersicht
Von Klasse geerbte verschachtelte Klassen/Schnittstellen org.deidentifier.arx.metric.Metric
Metric.AggregateFunction -
Konstruktorübersicht
Konstruktoren -
Methodenübersicht
Modifizierer und TypMethodeBeschreibungReturns an instance of the maximal value.Returns an instance of the minimal value.Returns the configuration of this metric.protected ILSingleDimensionalWithBoundgetInformationLossInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node, org.deidentifier.arx.framework.check.groupify.HashGroupify g) Evaluates the metric for the given node.protected ILSingleDimensionalWithBoundgetInformationLossInternal(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 ILSingleDimensionalgetLowerBoundInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node) Returns a lower bound for the information loss for the given node.protected ILSingleDimensionalgetLowerBoundInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node, org.deidentifier.arx.framework.check.groupify.HashGroupify g) Returns a lower bound for the information loss for the given node.getName()Returns the name of metric.protected voidinitializeInternal(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.render(ARXConfiguration config) Renders the privacy modeltoString()Returns the name of metric.Von Klasse geerbte Methoden org.deidentifier.arx.metric.v2.AbstractMetricSingleDimensional
createInformationLoss, createInformationLoss, getAggregationFunctionsGeneralized, getAggregationFunctionsNonGeneralized, getAggregationIndicesGeneralized, getAggregationIndicesNonGeneralized, getAggregationInformation, getNumTuples, setNumTuplesVon 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, getAggregateFunction, getDescription, getDescription, getGeneralizationFactor, getGeneralizationSuppressionFactor, getInformationLoss, getInformationLoss, getLowerBound, getLowerBound, getNumRecords, getScore, getSubset, getSuppressionFactor, initialize, isAbleToHandleClusteredMicroaggregation, isAbleToHandleMicroaggregation, isGSFactorSupported, isIndependent, isMonotonic, isMonotonicWithGeneralization, isMonotonicWithSuppression, isMultiDimensional, isPrecomputed, isScoreFunctionSupported, isWeighted, list, round
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Konstruktordetails
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MetricSDNMKLDivergence
public MetricSDNMKLDivergence()Default constructor.
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Methodendetails
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createMaxInformationLoss
Beschreibung aus Klasse kopiert:MetricReturns an instance of the maximal value.- Setzt außer Kraft:
createMaxInformationLossin KlasseAbstractMetricSingleDimensional- Gibt zurück:
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createMinInformationLoss
Beschreibung aus Klasse kopiert:MetricReturns an instance of the minimal value.- Setzt außer Kraft:
createMinInformationLossin KlasseAbstractMetricSingleDimensional- Gibt zurück:
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getConfiguration
Returns the configuration of this metric.- Setzt außer Kraft:
getConfigurationin KlasseMetric<ILSingleDimensional>- Gibt zurück:
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getName
Beschreibung aus Klasse kopiert:MetricReturns the name of metric.- Setzt außer Kraft:
getNamein KlasseMetric<ILSingleDimensional>- Gibt zurück:
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render
Beschreibung aus Klasse kopiert:MetricRenders the privacy model- Angegeben von:
renderin KlasseMetric<ILSingleDimensional>- Gibt zurück:
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toString
Beschreibung aus Klasse kopiert:MetricReturns the name of metric.- Setzt außer Kraft:
toStringin KlasseMetric<ILSingleDimensional>- Gibt zurück:
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getInformationLossInternal
protected ILSingleDimensionalWithBound getInformationLossInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node, org.deidentifier.arx.framework.check.groupify.HashGroupify g) Beschreibung aus Klasse kopiert:MetricEvaluates the metric for the given node.- Angegeben von:
getInformationLossInternalin KlasseMetric<ILSingleDimensional>- Parameter:
node- The node for which to compute the information lossg- The groupify operator of the previous check- Gibt zurück:
- the double
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getInformationLossInternal
protected ILSingleDimensionalWithBound getInformationLossInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node, org.deidentifier.arx.framework.check.groupify.HashGroupifyEntry entry) Beschreibung aus Klasse kopiert:MetricReturns the information loss that would be induced by suppressing the given entry. The loss is not necessarily consistent with the loss that is computed bygetInformationLoss(node, groupify)but is guaranteed to be comparable for different entries from the same groupify operator.- Angegeben von:
getInformationLossInternalin KlasseMetric<ILSingleDimensional>- Parameter:
entry-- Gibt zurück:
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getLowerBoundInternal
protected ILSingleDimensional getLowerBoundInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node) Beschreibung aus Klasse kopiert:MetricReturns 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 returnnull.- Angegeben von:
getLowerBoundInternalin KlasseMetric<ILSingleDimensional>- Parameter:
node-- Gibt zurück:
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getLowerBoundInternal
protected ILSingleDimensional getLowerBoundInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node, org.deidentifier.arx.framework.check.groupify.HashGroupify g) Beschreibung aus Klasse kopiert:MetricReturns 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 returnnull.
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:
getLowerBoundInternalin KlasseMetric<ILSingleDimensional>- Parameter:
node-g-- Gibt zurück:
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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:MetricImplement this to initialize the metric.- Setzt außer Kraft:
initializeInternalin KlasseAbstractMetricSingleDimensional- Parameter:
manager-definition-input-hierarchies-config-
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