org.deidentifier.arx.metric.v2

Class MetricSDNMKLDivergence

java.lang.Object

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

org.deidentifier.arx.metric.v2.AbstractMetricSingleDimensional

org.deidentifier.arx.metric.v2.MetricSDNMKLDivergence

All Implemented Interfaces:

java.io.Serializable

public class MetricSDNMKLDivergence
extends AbstractMetricSingleDimensional

This class implements the KL Divergence metric. Ashwin Machanavajjhala, Daniel Kifer, Johannes Gehrke, Muthuramakrishnan Venkitasubramaniam: L-diversity: Privacy beyond k-anonymity ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 1 Issue 1, March 2007

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
MetricSDNMKLDivergence() Default constructor.

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.
protected ILSingleDimensionalWithBound	getInformationLossInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node, org.deidentifier.arx.framework.check.groupify.HashGroupify g) Evaluates the metric for the given node.
protected ILSingleDimensionalWithBound	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 ILSingleDimensional	getLowerBoundInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node) Returns a lower bound for the information loss for the given node.
protected ILSingleDimensional	getLowerBoundInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node, org.deidentifier.arx.framework.check.groupify.HashGroupify g) 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.
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, isAbleToHandleMicroaggregation, isGSFactorSupported, 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

MetricSDNMKLDivergence

public MetricSDNMKLDivergence()

Default constructor.

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:

getName

public java.lang.String getName()

Description copied from class: Metric

Returns the name of metric.

Overrides:

getName 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>

getInformationLossInternal

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

Description copied from class: Metric

Evaluates the metric for the given node.

Specified by:

getInformationLossInternal in class Metric<ILSingleDimensional>

Parameters:

node - The node for which to compute the information loss

g - The groupify operator of the previous check

Returns:

the double

getInformationLossInternal

protected ILSingleDimensionalWithBound 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<ILSingleDimensional>

Returns:

getLowerBoundInternal

protected ILSingleDimensional 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<ILSingleDimensional>

Returns:

getLowerBoundInternal

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

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