Package org.deidentifier.arx.metric.v2

Klasse MetricMDNMLoss

java.lang.Object

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

org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensional

org.deidentifier.arx.metric.v2.MetricMDNMLoss

Alle implementierten Schnittstellen:

Serializable

Bekannte direkte Unterklassen:

MetricMDNMLossPrecomputed

public class MetricMDNMLoss
extends AbstractMetricMultiDimensional

This class implements a variant of the Loss metric.

Siehe auch:

• Serialisierte Form

Verschachtelte Klassen - Übersicht

Von Klasse geerbte verschachtelte Klassen/Schnittstellen org.deidentifier.arx.metric.Metric

Metric.AggregateFunction

Feldübersicht

Von Klasse geerbte Felder org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensional

k

Konstruktorübersicht

Konstruktoren

Konstruktor	Beschreibung
MetricMDNMLoss()	Default constructor which treats all transformation methods equally.
MetricMDNMLoss(double gsFactor, Metric.AggregateFunction function)	A constructor that allows to define a factor weighting generalization and suppression.
MetricMDNMLoss(Metric.AggregateFunction function)	Default constructor which treats all transformation methods equally.

Methodenübersicht

Modifizierer und Typ	Methode	Beschreibung
MetricConfiguration	getConfiguration()	Returns the configuration of this metric.
double	getGeneralizationFactor()	Returns the factor used weight generalized values.
double	getGeneralizationSuppressionFactor()	Returns the factor weighting generalization and suppression.
protected ILMultiDimensionalWithBound	getInformationLossInternal(Transformation<?> node, HashGroupify g)	Evaluates the metric for the given node.
protected ILMultiDimensionalWithBound	getInformationLossInternal(Transformation<?> node, HashGroupifyEntry entry)	Returns the information loss that would be induced by suppressing the given entry.
protected AbstractILMultiDimensional	getLowerBoundInternal(Transformation<?> node)	Returns a lower bound for the information loss for the given node.
protected AbstractILMultiDimensional	getLowerBoundInternal(Transformation<?> node, HashGroupify g)	Returns a lower bound for the information loss for the given node.
String	getName()	Returns the name of metric.
ILScore	getScore(Transformation<?> node, HashGroupify groupify)	Calculates the score.
protected DomainShare[]	getShares()	For subclasses.
double	getSuppressionFactor()	Returns the factor used to weight suppressed values.
protected void	initializeInternal(DataManager manager, DataDefinition definition, Data input, GeneralizationHierarchy[] hierarchies, ARXConfiguration config)	Implement this to initialize the metric.
boolean	isAbleToHandleMicroaggregation()	Returns whether this metric handles microaggregation
boolean	isGSFactorSupported()	Returns whether a generalization/suppression factor is supported
boolean	isScoreFunctionSupported()	Returns whether the metric provides a score function
protected double	normalizeAggregated(double aggregate)	Normalizes the aggregate.
protected double	normalizeGeneralized(double aggregate, int dimension)	Normalizes the aggregate.
ElementData	render(ARXConfiguration config)	Renders the privacy model
String	toString()	Returns the name of metric.

Von Klasse geerbte Methoden org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensional

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

Von Klasse geerbte Methoden 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, getInformationLoss, getInformationLoss, getLowerBound, getLowerBound, getNumRecords, getSubset, initialize, isAbleToHandleClusteredMicroaggregation, isIndependent, isMonotonic, isMonotonicWithGeneralization, isMonotonicWithSuppression, isMultiDimensional, isPrecomputed, isWeighted, list, round

Von Klasse geerbte Methoden java.lang.Object

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

Konstruktordetails

MetricMDNMLoss

public MetricMDNMLoss()

Default constructor which treats all transformation methods equally.

MetricMDNMLoss

public MetricMDNMLoss(Metric.AggregateFunction function)

Default constructor which treats all transformation methods equally.

Parameter:

function -

MetricMDNMLoss

public MetricMDNMLoss(double gsFactor,
 Metric.AggregateFunction function)

A constructor that allows to define a factor weighting generalization and suppression.

Parameter:

gsFactor - 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.

function -

Methodendetails

getConfiguration

public MetricConfiguration getConfiguration()

Returns the configuration of this metric.

Setzt außer Kraft:

getConfiguration in Klasse Metric<AbstractILMultiDimensional>

Gibt zurück:

getGeneralizationFactor

public double getGeneralizationFactor()

Beschreibung aus Klasse kopiert: Metric

Returns the factor used weight generalized values.

Setzt außer Kraft:

getGeneralizationFactor in Klasse Metric<AbstractILMultiDimensional>

Gibt zurück:

getGeneralizationSuppressionFactor

public double getGeneralizationSuppressionFactor()

Beschreibung aus Klasse kopiert: Metric

Returns the factor weighting generalization and suppression.

Setzt außer Kraft:

getGeneralizationSuppressionFactor in Klasse Metric<AbstractILMultiDimensional>

Gibt zurück:

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.

getName

public String getName()

Beschreibung aus Klasse kopiert: Metric

Returns the name of metric.

Setzt außer Kraft:

getName in Klasse Metric<AbstractILMultiDimensional>

Gibt zurück:

getScore

public ILScore getScore(Transformation<?> node,
 HashGroupify groupify)

Beschreibung aus Klasse kopiert: Metric

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

Setzt außer Kraft:

getScore in Klasse Metric<AbstractILMultiDimensional>

Parameter:

node -

groupify -

Gibt zurück:

getSuppressionFactor

public double getSuppressionFactor()

Beschreibung aus Klasse kopiert: Metric

Returns the factor used to weight suppressed values.

Setzt außer Kraft:

getSuppressionFactor in Klasse Metric<AbstractILMultiDimensional>

Gibt zurück:

isAbleToHandleMicroaggregation

public boolean isAbleToHandleMicroaggregation()

Beschreibung aus Klasse kopiert: Metric

Returns whether this metric handles microaggregation

Setzt außer Kraft:

isAbleToHandleMicroaggregation in Klasse Metric<AbstractILMultiDimensional>

Gibt zurück:

isGSFactorSupported

public boolean isGSFactorSupported()

Beschreibung aus Klasse kopiert: Metric

Returns whether a generalization/suppression factor is supported

Setzt außer Kraft:

isGSFactorSupported in Klasse Metric<AbstractILMultiDimensional>

Gibt zurück:

isScoreFunctionSupported

public boolean isScoreFunctionSupported()

Beschreibung aus Klasse kopiert: Metric

Returns whether the metric provides a score function

Setzt außer Kraft:

isScoreFunctionSupported in Klasse Metric<AbstractILMultiDimensional>

Gibt zurück:

render

public ElementData render(ARXConfiguration config)

Beschreibung aus Klasse kopiert: Metric

Renders the privacy model

Angegeben von:

render in Klasse Metric<AbstractILMultiDimensional>

Gibt zurück:

toString

public String toString()

Beschreibung aus Klasse kopiert: Metric

Returns the name of metric.

Setzt außer Kraft:

toString in Klasse Metric<AbstractILMultiDimensional>

Gibt zurück:

getInformationLossInternal

protected ILMultiDimensionalWithBound getInformationLossInternal(Transformation<?> node,
 HashGroupify g)

Beschreibung aus Klasse kopiert: Metric

Evaluates the metric for the given node.

Angegeben von:

getInformationLossInternal in Klasse Metric<AbstractILMultiDimensional>

Parameter:

node - The node for which to compute the information loss

g - The groupify operator of the previous check

Gibt zurück:

the double

getInformationLossInternal

protected ILMultiDimensionalWithBound getInformationLossInternal(Transformation<?> node,
 HashGroupifyEntry entry)

Beschreibung aus Klasse kopiert: 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.

Angegeben von:

getInformationLossInternal in Klasse Metric<AbstractILMultiDimensional>

Parameter:

entry -

Gibt zurück:

getLowerBoundInternal

protected AbstractILMultiDimensional getLowerBoundInternal(Transformation<?> node)

Beschreibung aus Klasse kopiert: 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.

Angegeben von:

getLowerBoundInternal in Klasse Metric<AbstractILMultiDimensional>

Parameter:

node -

Gibt zurück:

getLowerBoundInternal

protected AbstractILMultiDimensional getLowerBoundInternal(Transformation<?> node,
 HashGroupify g)

Beschreibung aus Klasse kopiert: 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!

Angegeben von:

getLowerBoundInternal in Klasse Metric<AbstractILMultiDimensional>

Parameter:

node -

g -

Gibt zurück:

getShares

protected DomainShare[] getShares()

For subclasses.

Gibt zurück:

initializeInternal

protected void initializeInternal(DataManager manager,
 DataDefinition definition,
 Data input,
 GeneralizationHierarchy[] hierarchies,
 ARXConfiguration config)

Beschreibung aus Klasse kopiert: Metric

Implement this to initialize the metric.

Setzt außer Kraft:

initializeInternal in Klasse AbstractMetricMultiDimensional

Parameter:

manager -

definition -

input -

hierarchies -

config -

normalizeAggregated

protected double normalizeAggregated(double aggregate)

Normalizes the aggregate.

Parameter:

aggregate -

dimension -

Gibt zurück:

normalizeGeneralized

protected double normalizeGeneralized(double aggregate,
 int dimension)

Normalizes the aggregate.

Parameter:

aggregate -

dimension -

Gibt zurück: