Package org.deidentifier.arx.metric.v2

Klasse MetricSDNMPublisherPayout

java.lang.Object

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

org.deidentifier.arx.metric.v2.AbstractMetricSingleDimensional

org.deidentifier.arx.metric.v2.MetricSDNMPublisherPayout

Alle implementierten Schnittstellen:

Serializable

public class MetricSDNMPublisherPayout
extends AbstractMetricSingleDimensional

This class implements a model which maximizes publisher benefit according to the model proposed in:
A Game Theoretic Framework for Analyzing Re-Identification Risk. Zhiyu Wan, Yevgeniy Vorobeychik, Weiyi Xia, Ellen Wright Clayton, Murat Kantarcioglu, Ranjit Ganta, Raymond Heatherly, Bradley A. Malin PLOS|ONE. 2015.

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	MetricSDNMPublisherPayout(boolean journalistAttackerModel, double gsFactor)	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.
ARXCostBenefitConfiguration	getCostBenefitConfiguration()	Returns the cost/benefit configuration
protected ILSingleDimensionalWithBound	getInformationLossInternal(Transformation<?> transformation, HashGroupify groupify)	Evaluates the metric for the given node.
protected InformationLossWithBound<ILSingleDimensional>	getInformationLossInternal(Transformation<?> transformation, HashGroupifyEntry entry)	Returns the information loss that would be induced by suppressing the given entry.
protected ILSingleDimensional	getLowerBoundInternal(Transformation<?> transformation)	Returns a lower bound for the information loss for the given node.
protected ILSingleDimensional	getLowerBoundInternal(Transformation<?> transformation, HashGroupify groupify)	Returns a lower bound for the information loss for the given node.
String	getName()	Returns the name of metric.
protected void	initializeInternal(DataManager manager, DataDefinition definition, Data input, GeneralizationHierarchy[] hierarchies, ARXConfiguration config)	Implement this to initialize the metric.
boolean	isAbleToHandleMicroaggregation()	Returns whether this metric handles microaggregation
boolean	isGSFactorSupported()	Returns whether a generalization/suppression factor is supported
boolean	isJournalistAttackerModel()	Returns whether the journalist attacker model is being assumed.
boolean	isProsecutorAttackerModel()	Returns whether the prosecutor attacker model is being assumed.
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, getNumRecords, getScore, getSubset, getSuppressionFactor, initialize, isAbleToHandleClusteredMicroaggregation, 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

MetricSDNMPublisherPayout

protected MetricSDNMPublisherPayout(boolean journalistAttackerModel,
 double gsFactor)

Creates a new instance

Parameter:

journalistAttackerModel - If set to true, the journalist attacker model will be assumed, the prosecutor model will be assumed, otherwise

gsFactor - 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.

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:

getCostBenefitConfiguration

public ARXCostBenefitConfiguration getCostBenefitConfiguration()

Returns the cost/benefit configuration

getName

public String getName()

Beschreibung aus Klasse kopiert: Metric

Returns the name of metric.

Setzt außer Kraft:

getName in Klasse Metric<ILSingleDimensional>

Gibt zurück:

isAbleToHandleMicroaggregation

public boolean isAbleToHandleMicroaggregation()

Beschreibung aus Klasse kopiert: Metric

Returns whether this metric handles microaggregation

Setzt außer Kraft:

isAbleToHandleMicroaggregation in Klasse Metric<ILSingleDimensional>

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 Metric<ILSingleDimensional>

Gibt zurück:

isJournalistAttackerModel

public boolean isJournalistAttackerModel()

Returns whether the journalist attacker model is being assumed.

Gibt zurück:

isProsecutorAttackerModel

public boolean isProsecutorAttackerModel()

Returns whether the prosecutor attacker model is being assumed.

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<?> transformation,
 HashGroupify groupify)

Beschreibung aus Klasse kopiert: Metric

Evaluates the metric for the given node.

Angegeben von:

getInformationLossInternal in Klasse Metric<ILSingleDimensional>

Parameter:

transformation - The node for which to compute the information loss

groupify - The groupify operator of the previous check

Gibt zurück:

the double

getInformationLossInternal

protected InformationLossWithBound<ILSingleDimensional> getInformationLossInternal(Transformation<?> transformation,
 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<?> transformation)

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:

transformation -

Gibt zurück:

getLowerBoundInternal

protected ILSingleDimensional getLowerBoundInternal(Transformation<?> transformation,
 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:

transformation -

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 -