org.deidentifier.arx.metric.v2

Class AbstractMetricMultiDimensionalPotentiallyPrecomputed

java.lang.Object

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

org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensional

org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensionalPotentiallyPrecomputed

All Implemented Interfaces:

java.io.Serializable

Direct Known Subclasses:

MetricMDNMLossPotentiallyPrecomputed, MetricMDNUEntropyPotentiallyPrecomputed, MetricMDNUNMEntropyPotentiallyPrecomputed, MetricMDNUNMNormalizedEntropyPotentiallyPrecomputed

public abstract class AbstractMetricMultiDimensionalPotentiallyPrecomputed
extends AbstractMetricMultiDimensional

This class provides an abstract skeleton for the implementation of metrics that can either be precomputed or not. The decision is made at runtime depending on data properties.

See Also:

Serialized Form

Nested Class Summary

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

Metric.AggregateFunction

Field Summary

Fields inherited from class org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensional

k

Method Summary

Modifier and Type	Method and Description
InformationLoss<?>	createMaxInformationLoss() Returns an instance of the maximal value.
InformationLoss<?>	createMinInformationLoss() Returns an instance of the minimal value.
Metric.AggregateFunction	getAggregateFunction() Returns the aggregate function of a multi-dimensional metric, null otherwise.
protected AbstractMetricMultiDimensional	getDefaultMetric() Returns the default variant.
double	getGeneralizationFactor() Returns the factor used weight generalized values.
double	getGeneralizationSuppressionFactor() Returns the factor weighting generalization and suppression.
protected InformationLossWithBound<AbstractILMultiDimensional>	getInformationLossInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node, org.deidentifier.arx.framework.check.groupify.HashGroupify groupify) Evaluates the metric for the given node.
protected InformationLossWithBound<AbstractILMultiDimensional>	getInformationLossInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node, org.deidentifier.arx.framework.check.groupify.HashGroupifyEntry entry) Returns the information loss that would be induced by suppressing the given entry.
protected AbstractILMultiDimensional	getLowerBoundInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node) Returns a lower bound for the information loss for the given node.
protected AbstractILMultiDimensional	getLowerBoundInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node, org.deidentifier.arx.framework.check.groupify.HashGroupify groupify) Returns a lower bound for the information loss for the given node.
protected AbstractMetricMultiDimensional	getPrecomputedMetric() Returns the precomputed variant.
ILScore	getScore(org.deidentifier.arx.framework.lattice.Transformation<?> node, org.deidentifier.arx.framework.check.groupify.HashGroupify groupify) Calculates the score.
double	getSuppressionFactor() Returns the factor used to weight suppressed values.
protected double	getThreshold() Returns the threshold.
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[] ahierarchies, ARXConfiguration config) Implement this to initialize the metric.
boolean	isIndependent() Returns whether this metric requires the transformed data or groups to determine information loss.
boolean	isPrecomputed() Returns whether the metric is precomputed
boolean	isScoreFunctionSupported() Returns whether the metric provides a score function

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

createInformationLoss, getAggregationFunctionsGeneralized, getAggregationFunctionsNonGeneralized, getAggregationIndicesGeneralized, getAggregationIndicesNonGeneralized, getAggregationInformation, getDimensions, getDimensionsAggregated, getDimensionsGeneralized, initialize, setMax, setMin

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, getConfiguration, getDescription, getDescription, getInformationLoss, getInformationLoss, getLowerBound, getLowerBound, getName, getNumRecords, getSubset, initialize, isAbleToHandleClusteredMicroaggregation, isAbleToHandleMicroaggregation, isGSFactorSupported, isMonotonic, isMonotonicWithGeneralization, isMonotonicWithSuppression, isMultiDimensional, isWeighted, list, render, round, toString

Methods inherited from class java.lang.Object

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

Method Detail

createMaxInformationLoss

public InformationLoss<?> createMaxInformationLoss()

Description copied from class: Metric

Returns an instance of the maximal value.

Overrides:

createMaxInformationLoss in class AbstractMetricMultiDimensional

Returns:

createMinInformationLoss

public InformationLoss<?> createMinInformationLoss()

Description copied from class: Metric

Returns an instance of the minimal value.

Overrides:

createMinInformationLoss in class AbstractMetricMultiDimensional

Returns:

getAggregateFunction

public Metric.AggregateFunction getAggregateFunction()

Description copied from class: Metric

Returns the aggregate function of a multi-dimensional metric, null otherwise.

Overrides:

getAggregateFunction in class AbstractMetricMultiDimensional

Returns:

getGeneralizationFactor

public double getGeneralizationFactor()

Description copied from class: Metric

Returns the factor used weight generalized values.

Overrides:

getGeneralizationFactor in class Metric<AbstractILMultiDimensional>

Returns:

getGeneralizationSuppressionFactor

public double getGeneralizationSuppressionFactor()

Description copied from class: Metric

Returns the factor weighting generalization and suppression.

Overrides:

getGeneralizationSuppressionFactor in class Metric<AbstractILMultiDimensional>

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.

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:

getSuppressionFactor

public double getSuppressionFactor()

Description copied from class: Metric

Returns the factor used to weight suppressed values.

Overrides:

getSuppressionFactor in class Metric<AbstractILMultiDimensional>

Returns:

isIndependent

public boolean isIndependent()

Description copied from class: Metric

Returns whether this metric requires the transformed data or groups to determine information loss.

Overrides:

isIndependent 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:

getDefaultMetric

protected AbstractMetricMultiDimensional getDefaultMetric()

Returns the default variant.

Returns:

getInformationLossInternal

protected InformationLossWithBound<AbstractILMultiDimensional> getInformationLossInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node,
                                                                                          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<AbstractILMultiDimensional>

Parameters:

node - The node for which to compute the information loss

groupify - The groupify operator of the previous check

Returns:

the double

getInformationLossInternal

protected InformationLossWithBound<AbstractILMultiDimensional> getInformationLossInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node,
                                                                                          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<AbstractILMultiDimensional>

Returns:

getLowerBoundInternal

protected AbstractILMultiDimensional getLowerBoundInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node)

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<AbstractILMultiDimensional>

Returns:

getLowerBoundInternal

protected AbstractILMultiDimensional getLowerBoundInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node,
                                                           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<AbstractILMultiDimensional>

Returns:

getPrecomputedMetric

protected AbstractMetricMultiDimensional getPrecomputedMetric()

Returns the precomputed variant.

Returns:

getThreshold

protected double getThreshold()

Returns the threshold.

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[] ahierarchies,
                                  ARXConfiguration config)

Description copied from class: Metric

Implement this to initialize the metric.

Overrides:

initializeInternal in class AbstractMetricMultiDimensional