org.deidentifier.arx.metric.v2

Class MetricSDNMEntropyBasedInformationLoss

java.lang.Object

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

org.deidentifier.arx.metric.v2.AbstractMetricSingleDimensional

org.deidentifier.arx.metric.v2.MetricSDNMEntropyBasedInformationLoss

All Implemented Interfaces:

java.io.Serializable

public class MetricSDNMEntropyBasedInformationLoss
extends AbstractMetricSingleDimensional

This class implements a the entropy-based information loss 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

Constructor and Description
MetricSDNMEntropyBasedInformationLoss() Creates a new instance.
MetricSDNMEntropyBasedInformationLoss(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.
static double	getEntropyBasedInformationLoss(org.deidentifier.arx.framework.lattice.Transformation<?> transformation, org.deidentifier.arx.framework.check.groupify.HashGroupifyEntry entry, DomainShare[] shares, org.deidentifier.arx.framework.data.DataAggregationInformation aggregation, double maxIL) Implements the entropy-based IL model.
static double	getMaximalEntropyBasedInformationLoss(DomainShare[] domainShares, org.deidentifier.arx.framework.data.DataAggregationInformation aggregation) Returns the maximal entropy-based information loss
java.lang.String	getName() Returns the name of metric.
boolean	isAbleToHandleMicroaggregation() Returns whether this metric handles microaggregation
boolean	isGSFactorSupported() Returns whether a generalization/suppression factor is supported
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

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, getGeneralizationFactor, getGeneralizationSuppressionFactor, getInformationLoss, getInformationLoss, getLowerBound, getLowerBound, getScore, getSuppressionFactor, initialize, isAbleToHandleClusteredMicroaggregation, isIndependent, isMonotonic, isMonotonicWithGeneralization, isMonotonicWithSuppression, isMultiDimensional, isPrecomputed, isScoreFunctionSupported, isWeighted, list

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

MetricSDNMEntropyBasedInformationLoss

public MetricSDNMEntropyBasedInformationLoss()

Creates a new instance. Default constructor which treats all transformation methods equally.

MetricSDNMEntropyBasedInformationLoss

public MetricSDNMEntropyBasedInformationLoss(double gsFactor)

Creates a new instance.

Parameters:

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

getEntropyBasedInformationLoss

public static double getEntropyBasedInformationLoss(org.deidentifier.arx.framework.lattice.Transformation<?> transformation,
                                                    org.deidentifier.arx.framework.check.groupify.HashGroupifyEntry entry,
                                                    DomainShare[] shares,
                                                    org.deidentifier.arx.framework.data.DataAggregationInformation aggregation,
                                                    double maxIL)

Implements the entropy-based IL model. Ignores record suppression. Returns the loss for exactly one record.

Parameters:

transformation -

entry -

shares -

aggregation -

maxIL -

Returns:

getMaximalEntropyBasedInformationLoss

public static double getMaximalEntropyBasedInformationLoss(DomainShare[] domainShares,
                                                           org.deidentifier.arx.framework.data.DataAggregationInformation aggregation)

Returns the maximal entropy-based information loss

Parameters:

domainShares - For generalized attributes

aggregation - For microaggregated attributes

Returns:

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:

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:

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>