org.deidentifier.arx.metric.v2

Class MetricMDNMLossPotentiallyPrecomputed

java.lang.Object

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

org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensional

org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensionalPotentiallyPrecomputed

org.deidentifier.arx.metric.v2.MetricMDNMLossPotentiallyPrecomputed

All Implemented Interfaces:

java.io.Serializable

public class MetricMDNMLossPotentiallyPrecomputed
extends AbstractMetricMultiDimensionalPotentiallyPrecomputed

This class implements a variant of the Loss metric. TODO: Add reference.

See Also:

Serialized Form

Nested Class Summary

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

Metric.AggregateFunction

Method Summary

Modifier and Type	Method and Description
MetricConfiguration	getConfiguration() Returns the configuration of this metric.
double	getGeneralizationFactor() Returns the factor used weight generalized values.
double	getGeneralizationSuppressionFactor() Returns the factor weighting generalization and suppression.
double	getSuppressionFactor() Returns the factor used to weight suppressed values.
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.AbstractMetricMultiDimensionalPotentiallyPrecomputed

createMaxInformationLoss, createMinInformationLoss, getAggregateFunction, getScore, isIndependent, isPrecomputed, isScoreFunctionSupported

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, getDescription, getInformationLoss, getInformationLoss, getLowerBound, getLowerBound, getName, initialize, isAbleToHandleClusteredMicroaggregation, isMonotonic, isMonotonicWithGeneralization, isMonotonicWithSuppression, isMultiDimensional, isWeighted, list

Methods inherited from class java.lang.Object

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

Method Detail

getConfiguration

public MetricConfiguration getConfiguration()

Returns the configuration of this metric.

Overrides:

getConfiguration in class Metric<AbstractILMultiDimensional>

Returns:

getGeneralizationFactor

public double getGeneralizationFactor()

Description copied from class: Metric

Returns the factor used weight generalized values.

Overrides:

getGeneralizationFactor in class AbstractMetricMultiDimensionalPotentiallyPrecomputed

Returns:

getGeneralizationSuppressionFactor

public double getGeneralizationSuppressionFactor()

Description copied from class: Metric

Returns the factor weighting generalization and suppression.

Overrides:

getGeneralizationSuppressionFactor in class AbstractMetricMultiDimensionalPotentiallyPrecomputed

Returns:

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.

getSuppressionFactor

public double getSuppressionFactor()

Description copied from class: Metric

Returns the factor used to weight suppressed values.

Overrides:

getSuppressionFactor in class AbstractMetricMultiDimensionalPotentiallyPrecomputed

Returns:

isAbleToHandleMicroaggregation

public boolean isAbleToHandleMicroaggregation()

Description copied from class: Metric

Returns whether this metric handles microaggregation

Overrides:

isAbleToHandleMicroaggregation in class Metric<AbstractILMultiDimensional>

Returns:

isGSFactorSupported

public boolean isGSFactorSupported()

Description copied from class: Metric

Returns whether a generalization/suppression factor is supported

Overrides:

isGSFactorSupported in class Metric<AbstractILMultiDimensional>

Returns:

render

public ElementData render(ARXConfiguration config)

Description copied from class: Metric

Renders the privacy model

Specified by:

render in class Metric<AbstractILMultiDimensional>

Returns:

toString

public java.lang.String toString()

Description copied from class: Metric

Returns the name of metric.

Overrides:

toString in class Metric<AbstractILMultiDimensional>