org.deidentifier.arx.metric.v2

Class MetricSDNMPublisherPayout

java.lang.Object

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

org.deidentifier.arx.metric.v2.AbstractMetricSingleDimensional

org.deidentifier.arx.metric.v2.MetricSDNMPublisherPayout

All Implemented Interfaces:

java.io.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.

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class org.deidentifier.arx.metric.Metric

Metric.AggregateFunction

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	MetricSDNMPublisherPayout(boolean journalistAttackerModel, double gsFactor) Creates a new instance

Method Summary

Modifier and Type	Method and Description
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(org.deidentifier.arx.framework.lattice.Transformation<?> transformation, org.deidentifier.arx.framework.check.groupify.HashGroupify groupify) Evaluates the metric for the given node.
protected InformationLossWithBound<ILSingleDimensional>	getInformationLossInternal(org.deidentifier.arx.framework.lattice.Transformation<?> transformation, org.deidentifier.arx.framework.check.groupify.HashGroupifyEntry entry) Returns the information loss that would be induced by suppressing the given entry.
protected ILSingleDimensional	getLowerBoundInternal(org.deidentifier.arx.framework.lattice.Transformation<?> transformation) Returns a lower bound for the information loss for the given node.
protected ILSingleDimensional	getLowerBoundInternal(org.deidentifier.arx.framework.lattice.Transformation<?> transformation, org.deidentifier.arx.framework.check.groupify.HashGroupify groupify) Returns a lower bound for the information loss for the given node.
java.lang.String	getName() Returns the name of metric.
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.
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
java.lang.String	toString() Returns the name of metric.

Methods inherited from class org.deidentifier.arx.metric.v2.AbstractMetricSingleDimensional

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

Methods inherited from class 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

Methods inherited from class java.lang.Object

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

Constructor Detail

MetricSDNMPublisherPayout

protected MetricSDNMPublisherPayout(boolean journalistAttackerModel,
                                    double gsFactor)

Creates a new instance

Parameters:

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.

Method Detail

createMaxInformationLoss

public ILSingleDimensional createMaxInformationLoss()

Description copied from class: Metric

Returns an instance of the maximal value.

Overrides:

createMaxInformationLoss in class AbstractMetricSingleDimensional

Returns:

createMinInformationLoss

public ILSingleDimensional createMinInformationLoss()

Description copied from class: Metric

Returns an instance of the minimal value.

Overrides:

createMinInformationLoss in class AbstractMetricSingleDimensional

Returns:

getConfiguration

public MetricConfiguration getConfiguration()

Returns the configuration of this metric.

Overrides:

getConfiguration in class Metric<ILSingleDimensional>

Returns:

getCostBenefitConfiguration

public ARXCostBenefitConfiguration getCostBenefitConfiguration()

Returns the cost/benefit configuration

getName

public java.lang.String getName()

Description copied from class: Metric

Returns the name of metric.

Overrides:

getName in class Metric<ILSingleDimensional>

Returns:

isAbleToHandleMicroaggregation

public boolean isAbleToHandleMicroaggregation()

Description copied from class: Metric

Returns whether this metric handles microaggregation

Overrides:

isAbleToHandleMicroaggregation in class Metric<ILSingleDimensional>

Returns:

isGSFactorSupported

public boolean isGSFactorSupported()

Description copied from class: Metric

Returns whether a generalization/suppression factor is supported

Overrides:

isGSFactorSupported in class Metric<ILSingleDimensional>

Returns:

isJournalistAttackerModel

public boolean isJournalistAttackerModel()

Returns whether the journalist attacker model is being assumed.

Returns:

isProsecutorAttackerModel

public boolean isProsecutorAttackerModel()

Returns whether the prosecutor attacker model is being assumed.

Returns:

render

public ElementData render(ARXConfiguration config)

Description copied from class: Metric

Renders the privacy model

Specified by:

render in class Metric<ILSingleDimensional>

Returns:

toString

public java.lang.String toString()

Description copied from class: Metric

Returns the name of metric.

Overrides:

toString in class Metric<ILSingleDimensional>

getInformationLossInternal

protected ILSingleDimensionalWithBound getInformationLossInternal(org.deidentifier.arx.framework.lattice.Transformation<?> transformation,
                                                                  org.deidentifier.arx.framework.check.groupify.HashGroupify groupify)

Description copied from class: Metric

Evaluates the metric for the given node.

Specified by:

getInformationLossInternal in class Metric<ILSingleDimensional>

Parameters:

transformation - The node for which to compute the information loss

groupify - The groupify operator of the previous check

Returns:

the double

getInformationLossInternal

protected InformationLossWithBound<ILSingleDimensional> getInformationLossInternal(org.deidentifier.arx.framework.lattice.Transformation<?> transformation,
                                                                                   org.deidentifier.arx.framework.check.groupify.HashGroupifyEntry entry)

Description copied from class: 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.

Specified by:

getInformationLossInternal in class Metric<ILSingleDimensional>

Returns:

getLowerBoundInternal

protected ILSingleDimensional getLowerBoundInternal(org.deidentifier.arx.framework.lattice.Transformation<?> transformation)

Description copied from class: 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.

Specified by:

getLowerBoundInternal in class Metric<ILSingleDimensional>

Returns:

getLowerBoundInternal

protected ILSingleDimensional getLowerBoundInternal(org.deidentifier.arx.framework.lattice.Transformation<?> transformation,
                                                    org.deidentifier.arx.framework.check.groupify.HashGroupify groupify)

Description copied from class: 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!

Specified by:

getLowerBoundInternal in class Metric<ILSingleDimensional>

Returns:

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)

Description copied from class: Metric

Implement this to initialize the metric.

Overrides:

initializeInternal in class AbstractMetricSingleDimensional