org.deidentifier.arx.metric.v2

Class AbstractMetricMultiDimensional

java.lang.Object

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

org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensional

All Implemented Interfaces:

java.io.Serializable

Direct Known Subclasses:

AbstractMetricMultiDimensionalPotentiallyPrecomputed, MetricMDHeight, MetricMDNMLoss, MetricMDNMPrecision, MetricMDNUEntropyPrecomputed, MetricMDStatic

public abstract class AbstractMetricMultiDimensional
extends Metric<AbstractILMultiDimensional>

This class provides an abstract skeleton for the implementation of multi-dimensional metrics.

See Also:

Serialized Form

Nested Class Summary

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

Metric.AggregateFunction

Field Summary

Fields

Modifier and Type	Field and Description
protected int	k Minimal size of equivalence classes enforced by the differential privacy model

Method Summary

Modifier and Type	Method and Description
protected AbstractILMultiDimensional	createInformationLoss(double[] values) Helper method for creating information loss.
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 org.deidentifier.arx.framework.check.distribution.DistributionAggregateFunction[]	getAggregationFunctionsGeneralized() Returns relevant aggregation functions
protected org.deidentifier.arx.framework.check.distribution.DistributionAggregateFunction[]	getAggregationFunctionsNonGeneralized() Returns relevant aggregation functions
protected int[]	getAggregationIndicesGeneralized() Returns the indicies of aggregated variables
protected int[]	getAggregationIndicesNonGeneralized() Returns the indicies of aggregated variables
protected org.deidentifier.arx.framework.data.DataAggregationInformation	getAggregationInformation() Needed for microaggregation
protected int	getDimensions() Returns the number of dimensions.
protected int	getDimensionsAggregated() Returns the number of dimensions.
protected int	getDimensionsGeneralized() Returns the number of dimensions.
protected void	initialize(int dimensions) For backwards compatibility only.
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.
protected void	setMax(double[] max) Sets the maximal information loss.
protected void	setMin(double[] min) Sets the minimal information loss.

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, getGeneralizationFactor, getGeneralizationSuppressionFactor, getInformationLoss, getInformationLoss, getInformationLossInternal, getInformationLossInternal, getLowerBound, getLowerBound, getLowerBoundInternal, getLowerBoundInternal, getName, getNumRecords, getScore, getSubset, getSuppressionFactor, initialize, isAbleToHandleClusteredMicroaggregation, isAbleToHandleMicroaggregation, isGSFactorSupported, isIndependent, isMonotonic, isMonotonicWithGeneralization, isMonotonicWithSuppression, isMultiDimensional, isPrecomputed, isScoreFunctionSupported, isWeighted, list, render, round, toString

Methods inherited from class java.lang.Object

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

Field Detail

k

protected int k

Minimal size of equivalence classes enforced by the differential privacy model

Method Detail

createMaxInformationLoss

public InformationLoss<?> createMaxInformationLoss()

Description copied from class: Metric

Returns an instance of the maximal value.

Specified by:

createMaxInformationLoss in class Metric<AbstractILMultiDimensional>

Returns:

createMinInformationLoss

public InformationLoss<?> createMinInformationLoss()

Description copied from class: Metric

Returns an instance of the minimal value.

Specified by:

createMinInformationLoss in class Metric<AbstractILMultiDimensional>

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 Metric<AbstractILMultiDimensional>

Returns:

createInformationLoss

protected AbstractILMultiDimensional createInformationLoss(double[] values)

Helper method for creating information loss.

Parameters:

values -

Returns:

getAggregationFunctionsGeneralized

protected org.deidentifier.arx.framework.check.distribution.DistributionAggregateFunction[] getAggregationFunctionsGeneralized()

Returns relevant aggregation functions

Returns:

getAggregationFunctionsNonGeneralized

protected org.deidentifier.arx.framework.check.distribution.DistributionAggregateFunction[] getAggregationFunctionsNonGeneralized()

Returns relevant aggregation functions

Returns:

getAggregationIndicesGeneralized

protected int[] getAggregationIndicesGeneralized()

Returns the indicies of aggregated variables

Returns:

getAggregationIndicesNonGeneralized

protected int[] getAggregationIndicesNonGeneralized()

Returns the indicies of aggregated variables

Returns:

getAggregationInformation

protected org.deidentifier.arx.framework.data.DataAggregationInformation getAggregationInformation()

Needed for microaggregation

Returns:

getDimensions

protected int getDimensions()

Returns the number of dimensions.

Returns:

getDimensionsAggregated

protected int getDimensionsAggregated()

Returns the number of dimensions.

Returns:

getDimensionsGeneralized

protected int getDimensionsGeneralized()

Returns the number of dimensions.

Returns:

initialize

protected void initialize(int dimensions)

For backwards compatibility only.

Parameters:

dimensions -

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.

Specified by:

initializeInternal in class Metric<AbstractILMultiDimensional>

setMax

protected void setMax(double[] max)

Sets the maximal information loss.

Parameters:

max -

setMin

protected void setMin(double[] min)

Sets the minimal information loss.

Parameters:

min -