Package org.deidentifier.arx.metric.v2

Klasse MetricMDNMPrecision

java.lang.Object

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

org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensional

org.deidentifier.arx.metric.v2.MetricMDNMPrecision

Alle implementierten Schnittstellen:

Serializable

Bekannte direkte Unterklassen:

MetricMDPrecision

public class MetricMDNMPrecision
extends AbstractMetricMultiDimensional

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.

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

Modifizierer	Konstruktor	Beschreibung
protected	MetricMDNMPrecision()	Creates a new instance.
protected	MetricMDNMPrecision(boolean monotonicWithGeneralization, boolean monotonicWithSuppression, boolean independent, double gsFactor, Metric.AggregateFunction function)	For subclasses.
protected	MetricMDNMPrecision(boolean monotonicWithGeneralization, boolean monotonicWithSuppression, boolean independent, Metric.AggregateFunction function)	For subclasses.
protected	MetricMDNMPrecision(double gsFactor, Metric.AggregateFunction function)	Creates a new instance.
protected	MetricMDNMPrecision(Metric.AggregateFunction function)	Creates a new instance.

Methodenübersicht

Modifizierer und Typ	Methode	Beschreibung
MetricConfiguration	getConfiguration()	Returns the configuration of this metric.
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 groupify)	Returns a lower bound for the information loss for the given node.
ILScore	getScore(Transformation<?> node, HashGroupify groupify)	Calculates the score.
protected void	initialize(int[] heights, double cells)	For backwards compatibility only.
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
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, getGeneralizationFactor, getGeneralizationSuppressionFactor, getInformationLoss, getInformationLoss, getLowerBound, getLowerBound, getName, getNumRecords, getSubset, getSuppressionFactor, 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

MetricMDNMPrecision

protected MetricMDNMPrecision()

Creates a new instance.

MetricMDNMPrecision

protected MetricMDNMPrecision(Metric.AggregateFunction function)

Creates a new instance.

Parameter:

function -

MetricMDNMPrecision

protected MetricMDNMPrecision(boolean monotonicWithGeneralization,
 boolean monotonicWithSuppression,
 boolean independent,
 Metric.AggregateFunction function)

For subclasses.

Parameter:

monotonicWithGeneralization -

monotonicWithSuppression -

independent -

function -

MetricMDNMPrecision

protected MetricMDNMPrecision(boolean monotonicWithGeneralization,
 boolean monotonicWithSuppression,
 boolean independent,
 double gsFactor,
 Metric.AggregateFunction function)

For subclasses.

Parameter:

monotonicWithGeneralization -

monotonicWithSuppression -

independent -

gsFactor -

function -

MetricMDNMPrecision

protected MetricMDNMPrecision(double gsFactor,
 Metric.AggregateFunction function)

Creates a new instance.

Parameter:

gsFactor -

function -

Methodendetails

getConfiguration

public MetricConfiguration getConfiguration()

Returns the configuration of this metric.

Setzt außer Kraft:

getConfiguration 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:

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 groupify)

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 -

groupify -

Gibt zurück:

initialize

protected void initialize(int[] heights,
 double cells)

For backwards compatibility only.

Parameter:

heights -

cells -

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 -