org.deidentifier.arx.metric.v2

Class MetricMDNUEntropyPrecomputed

java.lang.Object

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

org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensional

org.deidentifier.arx.metric.v2.MetricMDNUEntropyPrecomputed

All Implemented Interfaces:

java.io.Serializable

Direct Known Subclasses:

MetricMDNUEntropy, MetricMDNUNMEntropyPrecomputed

public class MetricMDNUEntropyPrecomputed
extends AbstractMetricMultiDimensional

This class provides an efficient implementation of the non-uniform entropy metric. It avoids a cell-by-cell process by utilizing a three-dimensional array that maps identifiers to their frequency for all quasi-identifiers and generalization levels. It further reduces the overhead induced by subsequent calls by caching the results for previous columns and generalization levels. See:
A. De Waal and L. Willenborg: "Information loss through global recoding and local suppression" Netherlands Off Stat, vol. 14, pp. 17–20, 1999.

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
MetricMDNUEntropyPrecomputed(boolean monotonicWithGeneralization, boolean monotonicWithSuppression, boolean independent, double gsFactor, Metric.AggregateFunction function) Precomputed.

Method Summary

Modifier and Type	Method and Description
MetricConfiguration	getConfiguration() Returns the configuration of this metric.
ILScore	getScore(org.deidentifier.arx.framework.lattice.Transformation<?> node, org.deidentifier.arx.framework.check.groupify.HashGroupify groupify) Calculates the score.
boolean	isGSFactorSupported() Returns whether a generalization/suppression factor is supported
boolean	isPrecomputed() Returns whether the metric is precomputed
boolean	isScoreFunctionSupported() Returns whether the metric provides a score function
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.AbstractMetricMultiDimensional

createMaxInformationLoss, createMinInformationLoss, getAggregateFunction

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, getGeneralizationFactor, getGeneralizationSuppressionFactor, getInformationLoss, getInformationLoss, getLowerBound, getLowerBound, getName, getSuppressionFactor, initialize, isAbleToHandleClusteredMicroaggregation, isAbleToHandleMicroaggregation, isIndependent, isMonotonic, isMonotonicWithGeneralization, isMonotonicWithSuppression, isMultiDimensional, isWeighted, list

Methods inherited from class java.lang.Object

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

Constructor Detail

MetricMDNUEntropyPrecomputed

public MetricMDNUEntropyPrecomputed(boolean monotonicWithGeneralization,
                                    boolean monotonicWithSuppression,
                                    boolean independent,
                                    double gsFactor,
                                    Metric.AggregateFunction function)

Precomputed.

Parameters:

monotonicWithGeneralization -

monotonicWithSuppression -

independent -

gsFactor -

function -

Method Detail

getConfiguration

public MetricConfiguration getConfiguration()

Returns the configuration of this metric.

Overrides:

getConfiguration in class Metric<AbstractILMultiDimensional>

Returns:

getScore

public ILScore getScore(org.deidentifier.arx.framework.lattice.Transformation<?> node,
                        org.deidentifier.arx.framework.check.groupify.HashGroupify groupify)

Description copied from class: Metric

Calculates the score. Note: All score functions are expected to return a score value divided by the sensitivity of the score function.

Overrides:

getScore 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:

isPrecomputed

public boolean isPrecomputed()

Description copied from class: Metric

Returns whether the metric is precomputed

Overrides:

isPrecomputed in class Metric<AbstractILMultiDimensional>

Returns:

isScoreFunctionSupported

public boolean isScoreFunctionSupported()

Description copied from class: Metric

Returns whether the metric provides a score function

Overrides:

isScoreFunctionSupported 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>