Package org.deidentifier.arx.metric
Klasse MetricNMPrecision
java.lang.Object
org.deidentifier.arx.metric.Metric<org.deidentifier.arx.metric.InformationLossDefault>
org.deidentifier.arx.metric.MetricWeighted<org.deidentifier.arx.metric.InformationLossDefault>
org.deidentifier.arx.metric.MetricNMPrecision
- Alle implementierten Schnittstellen:
Serializable
public class MetricNMPrecision
extends MetricWeighted<org.deidentifier.arx.metric.InformationLossDefault>
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.
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:
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Verschachtelte Klassen - Übersicht
Von Klasse geerbte verschachtelte Klassen/Schnittstellen org.deidentifier.arx.metric.Metric
Metric.AggregateFunction -
Methodenübersicht
Modifizierer und TypMethodeBeschreibungReturns an instance of the maximal value.Returns an instance of the minimal value.render(ARXConfiguration config) Renders the privacy modeltoString()Returns the name of metric.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, getAggregateFunction, getConfiguration, getDescription, getGeneralizationFactor, getGeneralizationSuppressionFactor, getInformationLoss, getInformationLoss, getLowerBound, getLowerBound, getName, getScore, getSuppressionFactor, initialize, isAbleToHandleClusteredMicroaggregation, isAbleToHandleMicroaggregation, isGSFactorSupported, isIndependent, isMonotonic, isMonotonicWithGeneralization, isMonotonicWithSuppression, isMultiDimensional, isPrecomputed, isScoreFunctionSupported, isWeighted, list
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Methodendetails
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createMaxInformationLoss
Beschreibung aus Klasse kopiert:MetricReturns an instance of the maximal value.- Angegeben von:
createMaxInformationLossin KlasseMetric<org.deidentifier.arx.metric.InformationLossDefault>- Gibt zurück:
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createMinInformationLoss
Beschreibung aus Klasse kopiert:MetricReturns an instance of the minimal value.- Angegeben von:
createMinInformationLossin KlasseMetric<org.deidentifier.arx.metric.InformationLossDefault>- Gibt zurück:
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render
Beschreibung aus Klasse kopiert:MetricRenders the privacy model -
toString
Beschreibung aus Klasse kopiert:MetricReturns the name of metric.
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