Package org.deidentifier.arx.metric.v2

Klasse MetricSDNMDiscernability

java.lang.Object

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

org.deidentifier.arx.metric.v2.AbstractMetricSingleDimensional

org.deidentifier.arx.metric.v2.MetricSDNMDiscernability

Alle implementierten Schnittstellen:

Serializable

Bekannte direkte Unterklassen:

MetricSDDiscernability

public class MetricSDNMDiscernability
extends AbstractMetricSingleDimensional

This class provides an implementation of the non-monotonic DM metric. TODO: Add reference

Siehe auch:

• Serialisierte Form

Verschachtelte Klassen - Übersicht

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

Metric.AggregateFunction

Konstruktorübersicht

Konstruktoren

Modifizierer	Konstruktor	Beschreibung
protected	MetricSDNMDiscernability()	Creates a new instance.

Methodenübersicht

Modifizierer und Typ	Methode	Beschreibung
ILSingleDimensional	createMaxInformationLoss()	Returns an instance of the maximal value.
ILSingleDimensional	createMinInformationLoss()	Returns an instance of the minimal value.
MetricConfiguration	getConfiguration()	Returns the configuration of this metric.
protected ILSingleDimensionalWithBound	getInformationLossInternal(Transformation<?> node, HashGroupify g)	Evaluates the metric for the given node.
protected ILSingleDimensionalWithBound	getInformationLossInternal(Transformation<?> node, HashGroupifyEntry entry)	Returns the information loss that would be induced by suppressing the given entry.
protected ILSingleDimensional	getLowerBoundInternal(Transformation<?> node)	Returns a lower bound for the information loss for the given node.
protected ILSingleDimensional	getLowerBoundInternal(Transformation<?> node, HashGroupify groupify)	Returns a lower bound for the information loss for the given node.
ILScore	getScore(Transformation<?> node, HashGroupify groupify)	Calculates the score.
protected void	initializeInternal(DataManager manager, DataDefinition definition, Data input, GeneralizationHierarchy[] hierarchies, ARXConfiguration config)	Implement this to initialize the metric.
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.AbstractMetricSingleDimensional

createInformationLoss, createInformationLoss, getAggregationFunctionsGeneralized, getAggregationFunctionsNonGeneralized, getAggregationIndicesGeneralized, getAggregationIndicesNonGeneralized, getAggregationInformation, getNumTuples, setNumTuples

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, getAggregateFunction, getDescription, getDescription, getGeneralizationFactor, getGeneralizationSuppressionFactor, getInformationLoss, getInformationLoss, getLowerBound, getLowerBound, getName, getNumRecords, getSubset, getSuppressionFactor, initialize, isAbleToHandleClusteredMicroaggregation, isAbleToHandleMicroaggregation, isGSFactorSupported, isIndependent, isMonotonic, isMonotonicWithGeneralization, isMonotonicWithSuppression, isMultiDimensional, isPrecomputed, isWeighted, list, round

Von Klasse geerbte Methoden java.lang.Object

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

Konstruktordetails

MetricSDNMDiscernability

protected MetricSDNMDiscernability()

Creates a new instance.

Methodendetails

createMaxInformationLoss

public ILSingleDimensional createMaxInformationLoss()

Beschreibung aus Klasse kopiert: Metric

Returns an instance of the maximal value.

Setzt außer Kraft:

createMaxInformationLoss in Klasse AbstractMetricSingleDimensional

Gibt zurück:

createMinInformationLoss

public ILSingleDimensional createMinInformationLoss()

Beschreibung aus Klasse kopiert: Metric

Returns an instance of the minimal value.

Setzt außer Kraft:

createMinInformationLoss in Klasse AbstractMetricSingleDimensional

Gibt zurück:

getConfiguration

public MetricConfiguration getConfiguration()

Returns the configuration of this metric.

Setzt außer Kraft:

getConfiguration in Klasse Metric<ILSingleDimensional>

Gibt zurück:

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<ILSingleDimensional>

Parameter:

node -

groupify -

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<ILSingleDimensional>

Gibt zurück:

render

public ElementData render(ARXConfiguration config)

Beschreibung aus Klasse kopiert: Metric

Renders the privacy model

Angegeben von:

render in Klasse Metric<ILSingleDimensional>

Gibt zurück:

toString

public String toString()

Beschreibung aus Klasse kopiert: Metric

Returns the name of metric.

Setzt außer Kraft:

toString in Klasse Metric<ILSingleDimensional>

Gibt zurück:

getInformationLossInternal

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

Beschreibung aus Klasse kopiert: Metric

Evaluates the metric for the given node.

Angegeben von:

getInformationLossInternal in Klasse Metric<ILSingleDimensional>

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 ILSingleDimensionalWithBound 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<ILSingleDimensional>

Parameter:

entry -

Gibt zurück:

getLowerBoundInternal

protected ILSingleDimensional 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<ILSingleDimensional>

Parameter:

node -

Gibt zurück:

getLowerBoundInternal

protected ILSingleDimensional 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<ILSingleDimensional>

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 AbstractMetricSingleDimensional

Parameter:

manager -

definition -

input -

hierarchies -

config -