org.deidentifier.arx.metric.v2

Class MetricMDPrecision

java.lang.Object

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

org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensional

org.deidentifier.arx.metric.v2.MetricMDNMPrecision

org.deidentifier.arx.metric.v2.MetricMDPrecision

All Implemented Interfaces:

java.io.Serializable

public class MetricMDPrecision
extends MetricMDNMPrecision

This class provides an implementation of a weighted precision metric as proposed in:
Sweeney, L. (2002). Achieving k-anonymity privacy protection using generalization and suppression.
International Journal of Uncertainty Fuzziness and, 10(5), 2002.

This metric will respect attribute weights defined in the configuration.

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

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	MetricMDPrecision() Creates a new instance.
protected	MetricMDPrecision(double gsFactor, Metric.AggregateFunction function) Creates a new instance.
protected	MetricMDPrecision(Metric.AggregateFunction function) Creates a new instance.

Method Summary

Modifier and Type	Method and Description
MetricConfiguration	getConfiguration() Returns the configuration of this metric.
protected ILMultiDimensionalWithBound	getInformationLossInternal(org.deidentifier.arx.framework.lattice.Transformation<?> node, org.deidentifier.arx.framework.check.groupify.HashGroupify g) Evaluates the metric for the given node.
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.MetricMDNMPrecision

getInformationLossInternal, getLowerBoundInternal, getLowerBoundInternal, getScore, initialize, initializeInternal, isScoreFunctionSupported

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

createInformationLoss, createMaxInformationLoss, createMinInformationLoss, getAggregateFunction, 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, getDescription, getDescription, getGeneralizationFactor, getGeneralizationSuppressionFactor, getInformationLoss, getInformationLoss, getLowerBound, getLowerBound, getName, getNumRecords, getSubset, getSuppressionFactor, initialize, isAbleToHandleClusteredMicroaggregation, isIndependent, isMonotonic, isMonotonicWithGeneralization, isMonotonicWithSuppression, isMultiDimensional, isPrecomputed, isWeighted, list, round

Methods inherited from class java.lang.Object

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

Constructor Detail

MetricMDPrecision

protected MetricMDPrecision()

Creates a new instance.

MetricMDPrecision

protected MetricMDPrecision(Metric.AggregateFunction function)

Creates a new instance.

Parameters:

function -

MetricMDPrecision

protected MetricMDPrecision(double gsFactor,
                            Metric.AggregateFunction function)

Creates a new instance.

Parameters:

function -

Method Detail

getConfiguration

public MetricConfiguration getConfiguration()

Returns the configuration of this metric.

Overrides:

getConfiguration in class MetricMDNMPrecision

Returns:

isAbleToHandleMicroaggregation

public boolean isAbleToHandleMicroaggregation()

Description copied from class: Metric

Returns whether this metric handles microaggregation

Overrides:

isAbleToHandleMicroaggregation in class MetricMDNMPrecision

Returns:

isGSFactorSupported

public boolean isGSFactorSupported()

Description copied from class: Metric

Returns whether a generalization/suppression factor is supported

Overrides:

isGSFactorSupported in class MetricMDNMPrecision

Returns:

render

public ElementData render(ARXConfiguration config)

Description copied from class: Metric

Renders the privacy model

Overrides:

render in class MetricMDNMPrecision

Returns:

toString

public java.lang.String toString()

Description copied from class: Metric

Returns the name of metric.

Overrides:

toString in class MetricMDNMPrecision

getInformationLossInternal

protected ILMultiDimensionalWithBound 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.

Overrides:

getInformationLossInternal in class MetricMDNMPrecision

Parameters:

node - The node for which to compute the information loss

g - The groupify operator of the previous check

Returns:

the double