Package | Description |
---|---|
org.deidentifier.arx |
This package provides the public API for the ARX anonymization framework.
|
org.deidentifier.arx.framework.check |
This package implements the core functionality of the ARX framework, i.e., a highly efficient check for k-anonymity
|
org.deidentifier.arx.gui.model | |
org.deidentifier.arx.metric |
Package providing access to quality models
|
org.deidentifier.arx.metric.v2 |
Main package implementing quality models
|
org.deidentifier.arx.test |
Modifier and Type | Method and Description |
---|---|
Metric<?> |
ARXConfiguration.getQualityModel()
Returns the quality model to be used for optimizing output data.
|
Metric<?> |
ARXConfiguration.ARXConfigurationInternal.getQualityModel()
Returns the quality model to be used for optimizing output data.
|
Modifier and Type | Method and Description |
---|---|
static ARXConfiguration |
ARXConfiguration.create(double suppressionLimit,
Metric<?> metric)
Creates a new configuration that allows the given percentage of outliers and
thus implements tuple suppression.
|
static ARXConfiguration |
ARXConfiguration.create(Metric<?> metric)
Creates a new configuration that allows to define the metric for measuring information loss.
|
void |
ARXLattice.Access.setQualityModel(Metric<?> model)
Accessor method
|
void |
ARXConfiguration.setQualityModel(Metric<?> model)
Sets the quality model to be used for optimizing output data.
|
Constructor and Description |
---|
ARXResult(DataHandle handle,
DataDefinition definition,
ARXLattice lattice,
int historySize,
double snapshotSizeSnapshot,
double snapshotSizeDataset,
Metric<?> metric,
ARXConfiguration config,
ARXLattice.ARXNode optimum,
long time,
SolutionSpace<?> solutionSpace,
ARXProcessStatistics statistics)
Internal constructor for deserialization.
|
Modifier and Type | Method and Description |
---|---|
Metric<?> |
TransformationChecker.getMetric()
Returns the utility measure
|
Constructor and Description |
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TransformationApplicator(DataManager manager,
DataMatrix buffer,
Metric<?> metric,
ARXConfiguration.ARXConfigurationInternal config)
Creates a new transformation applicator instance.
|
TransformationChecker(DataManager manager,
Metric<?> metric,
ARXConfiguration.ARXConfigurationInternal config,
int historyMaxSize,
double snapshotSizeDataset,
double snapshotSizeSnapshot,
SolutionSpace<?> solutionSpace)
Creates a new transformation checker.
|
Modifier and Type | Method and Description |
---|---|
Metric<?> |
ModelConfiguration.getMetric()
Delegates to an instance of ARXConfiguration.
|
Modifier and Type | Method and Description |
---|---|
void |
ModelConfiguration.setMetric(Metric<?> metric)
Delegates to an instance of ARXConfiguration.
|
Modifier and Type | Class and Description |
---|---|
class |
MetricAECS
This class provides an implementation of the (normalized) average equivalence class size metric.
|
class |
MetricDefault
This class provides an abstract skeleton for the implementation of metrics.
|
class |
MetricDM
This class provides an implementation of the DM metric (non-monotonic).
|
class |
MetricDMStar
This class provides an implementation of the DM* metric (monotonic variant of
the Discernability Metric).
|
class |
MetricEntropy
This class provides an efficient implementation of the non-uniform entropy
metric.
|
class |
MetricHeight
This class provides an implementation of the Height metric.
|
class |
MetricNMEntropy
This class provides an efficient implementation of a non-monotonic and
non-uniform entropy metric.
|
class |
MetricNMPrecision
This class provides an implementation of a weighted precision metric as
proposed in:
Sweeney, L. (2002). |
class |
MetricPrecision
This class provides an implementation of a monotonic weighted precision metric.
|
class |
MetricStatic
This class provides an implementation of a static metric in
which information loss is user-defined per generalization level.
|
class |
MetricWeighted<T extends InformationLoss<?>>
This class provides an abstract skeleton for the implementation of weighted metrics.
|
Modifier and Type | Method and Description |
---|---|
static Metric<ILSingleDimensional> |
Metric.createAECSMetric()
Creates a new instance of the AECS metric.
|
static Metric<ILSingleDimensional> |
Metric.createAECSMetric(double gsFactor)
Creates a new instance of the AECS metric.
|
static Metric<ILSingleDimensional> |
Metric.createAmbiguityMetric()
Creates an instance of the ambiguity metric.
|
static Metric<ILSingleDimensional> |
Metric.createClassificationMetric()
Creates an instance of the classification metric.
|
static Metric<ILSingleDimensional> |
Metric.createClassificationMetric(double gsFactor)
Creates an instance of the classification metric.
|
static Metric<ILSingleDimensional> |
Metric.createDiscernabilityMetric()
Creates an instance of the discernability metric.
|
static Metric<ILSingleDimensional> |
Metric.createDiscernabilityMetric(boolean monotonic)
Creates an instance of the discernability metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createEntropyMetric()
Creates an instance of the non-monotonic non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createEntropyMetric(boolean monotonic)
Creates an instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createEntropyMetric(boolean monotonic,
double gsFactor)
Creates an instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createEntropyMetric(boolean monotonic,
double gsFactor,
Metric.AggregateFunction function)
Creates an instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createEntropyMetric(boolean monotonic,
Metric.AggregateFunction function)
Creates an instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createEntropyMetric(double gsFactor)
Creates an instance of the non-monotonic non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createHeightMetric()
Creates an instance of the height metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createHeightMetric(Metric.AggregateFunction function)
Creates an instance of the height metric.
|
abstract Metric<?> |
MetricDescription.createInstance(MetricConfiguration config)
Creates an instance with the given configuration options.
|
static Metric<ILSingleDimensional> |
Metric.createKLDivergenceMetric()
Creates an instance of the KL Divergence metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createLossMetric()
Creates an instance of the loss metric which treats generalization and suppression equally.
|
static Metric<AbstractILMultiDimensional> |
Metric.createLossMetric(double gsFactor)
Creates an instance of the loss metric with factors for weighting generalization and suppression.
|
static Metric<AbstractILMultiDimensional> |
Metric.createLossMetric(double gsFactor,
Metric.AggregateFunction function)
Creates an instance of the loss metric with factors for weighting generalization and suppression.
|
static Metric<AbstractILMultiDimensional> |
Metric.createLossMetric(Metric.AggregateFunction function)
Creates an instance of the loss metric which treats generalization and suppression equally.
|
static Metric<?> |
Metric.createMetric(Metric<?> metric,
int minLevel,
int maxLevel)
This method supports backwards compatibility.
|
static Metric<AbstractILMultiDimensional> |
Metric.createNormalizedEntropyMetric()
Creates an instance of the normalized entropy metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createNormalizedEntropyMetric(Metric.AggregateFunction function)
Creates an instance of the normalized entropy metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createPrecisionMetric()
Creates an instance of the non-monotonic precision metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createPrecisionMetric(boolean monotonic)
Creates an instance of the precision metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createPrecisionMetric(boolean monotonic,
double gsFactor)
Creates an instance of the precision metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createPrecisionMetric(boolean monotonic,
double gsFactor,
Metric.AggregateFunction function)
Creates an instance of the precision metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createPrecisionMetric(boolean monotonic,
Metric.AggregateFunction function)
Creates an instance of the precision metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createPrecisionMetric(double gsFactor)
Creates an instance of the non-monotonic precision metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createPrecisionMetric(double gsFactor,
Metric.AggregateFunction function)
Creates an instance of the non-monotonic precision metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createPrecisionMetric(Metric.AggregateFunction function)
Creates an instance of the non-monotonic precision metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createPrecomputedEntropyMetric(double threshold)
Creates a potentially precomputed instance of the non-monotonic non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createPrecomputedEntropyMetric(double threshold,
boolean monotonic)
Creates a potentially precomputed instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createPrecomputedEntropyMetric(double threshold,
boolean monotonic,
double gsFactor)
Creates a potentially precomputed instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createPrecomputedEntropyMetric(double threshold,
boolean monotonic,
double gsFactor,
Metric.AggregateFunction function)
Creates a potentially precomputed instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createPrecomputedEntropyMetric(double threshold,
boolean monotonic,
Metric.AggregateFunction function)
Creates a potentially precomputed instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createPrecomputedEntropyMetric(double threshold,
double gsFactor)
Creates a potentially precomputed instance of the non-monotonic non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createPrecomputedLossMetric(double threshold)
Creates a potentially precomputed instance of the loss metric which treats generalization
and suppression equally.
|
static Metric<AbstractILMultiDimensional> |
Metric.createPrecomputedLossMetric(double threshold,
double gsFactor)
Creates a potentially precomputed instance of the loss metric with factors for weighting generalization and suppression.
|
static Metric<AbstractILMultiDimensional> |
Metric.createPrecomputedLossMetric(double threshold,
double gsFactor,
Metric.AggregateFunction function)
Creates a potentially precomputed instance of the loss metric with factors for weighting generalization and suppression.
|
static Metric<AbstractILMultiDimensional> |
Metric.createPrecomputedLossMetric(double threshold,
Metric.AggregateFunction function)
Creates a potentially precomputed instance of the loss metric which treats generalization and suppression equally.
|
static Metric<AbstractILMultiDimensional> |
Metric.createPrecomputedNormalizedEntropyMetric(double threshold)
Creates a potentially precomputed instance of the normalized entropy metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createPrecomputedNormalizedEntropyMetric(double threshold,
Metric.AggregateFunction function)
Creates a potentially precomputed instance of the normalized entropy metric.
|
static Metric<AbstractILMultiDimensional> |
Metric.createStaticMetric(java.util.Map<java.lang.String,java.util.List<java.lang.Double>> loss)
Creates an instance of a metric with statically defined information loss.
|
static Metric<AbstractILMultiDimensional> |
Metric.createStaticMetric(java.util.Map<java.lang.String,java.util.List<java.lang.Double>> loss,
Metric.AggregateFunction function)
Creates an instance of a metric with statically defined information loss.
|
Modifier and Type | Method and Description |
---|---|
static InformationLoss<?> |
InformationLoss.createInformationLoss(InformationLoss<?> loss,
Metric<?> metric,
int minLevel,
int maxLevel)
Converter method, converting information loss from version 1 to information loss from version 2,
if necessary.
|
static Metric<?> |
Metric.createMetric(Metric<?> metric,
int minLevel,
int maxLevel)
This method supports backwards compatibility.
|
abstract boolean |
MetricDescription.isInstance(Metric<?> metric)
Returns whether the given metric is an instance of this description.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractMetricMultiDimensional
This class provides an abstract skeleton for the implementation of multi-dimensional metrics.
|
class |
AbstractMetricMultiDimensionalPotentiallyPrecomputed
This class provides an abstract skeleton for the implementation of metrics
that can either be precomputed or not.
|
class |
AbstractMetricSingleDimensional
This class provides an abstract skeleton for the implementation of single-dimensional metrics.
|
class |
MetricMDHeight
This class provides an implementation of the Height metric.
|
class |
MetricMDNMLoss
This class implements a variant of the Loss metric.
|
class |
MetricMDNMLossPotentiallyPrecomputed
This class implements a variant of the Loss metric.
|
class |
MetricMDNMLossPrecomputed
This class implements a variant of the Loss metric.
|
class |
MetricMDNMPrecision
This class provides an implementation of a weighted precision metric as
proposed in:
Sweeney, L. (2002). |
class |
MetricMDNUEntropy
This class provides an implementation of the non-uniform entropy
metric.
|
class |
MetricMDNUEntropyPotentiallyPrecomputed
This class provides an implementation of the non-uniform entropy
metric.
|
class |
MetricMDNUEntropyPrecomputed
This class provides an efficient implementation of the non-uniform entropy
metric.
|
class |
MetricMDNUNMEntropy
This class provides an implementation of the non-uniform entropy
metric.
|
class |
MetricMDNUNMEntropyPotentiallyPrecomputed
This class provides an implementation of the non-uniform entropy
metric.
|
class |
MetricMDNUNMEntropyPrecomputed
This class provides an implementation of the non-uniform entropy
metric.
|
class |
MetricMDNUNMNormalizedEntropy
This class provides an implementation of normalized non-uniform entropy.
|
class |
MetricMDNUNMNormalizedEntropyPotentiallyPrecomputed
This class provides an implementation of normalized non-uniform entropy.
|
class |
MetricMDNUNMNormalizedEntropyPrecomputed
This class provides an efficient implementation of normalized non-uniform entropy.
|
class |
MetricMDPrecision
This class provides an implementation of a weighted precision metric as
proposed in:
Sweeney, L. (2002). |
class |
MetricMDStatic
This class provides an implementation of a static metric in
which information loss is user-defined per generalization level.
|
class |
MetricSDAECS
This class provides an implementation of the (normalized) average equivalence class size metric.
|
class |
MetricSDClassification
This class provides an implementation of the classification metric.
|
class |
MetricSDDiscernability
This class provides an implementation of the monotonic DM* metric.
|
class |
MetricSDNMAmbiguity
This class implements a variant of the Ambiguity metric.
|
class |
MetricSDNMDiscernability
This class provides an implementation of the non-monotonic DM metric.
|
class |
MetricSDNMEntropyBasedInformationLoss
This class implements a the entropy-based information loss model proposed in:
A Game Theoretic Framework for Analyzing Re-Identification Risk. |
class |
MetricSDNMKLDivergence
This class implements the KL Divergence metric.
|
class |
MetricSDNMPublisherPayout
This class implements a model which maximizes publisher benefit according to the model proposed in:
A Game Theoretic Framework for Analyzing Re-Identification Risk. |
Modifier and Type | Method and Description |
---|---|
static Metric<ILSingleDimensional> |
__MetricV2.createAECSMetric()
Creates a new instance of the AECS metric.
|
static Metric<ILSingleDimensional> |
__MetricV2.createAECSMetric(double gsFactor)
Creates a new instance of the AECS metric.
|
static Metric<ILSingleDimensional> |
__MetricV2.createAECSMetric(int rowCount)
Creates a new instance of the AECS metric.
|
static Metric<ILSingleDimensional> |
__MetricV2.createAmbiguityMetric()
Creates an instance of the ambiguity metric.
|
static Metric<ILSingleDimensional> |
__MetricV2.createClassificationMetric()
Creates an instance of the classification metric.
|
static Metric<ILSingleDimensional> |
__MetricV2.createClassificationMetric(double gsFactor)
Creates an instance of the classification metric.
|
static Metric<ILSingleDimensional> |
__MetricV2.createDiscernabilityMetric()
Creates an instance of the discernability metric.
|
static Metric<ILSingleDimensional> |
__MetricV2.createDiscernabilityMetric(boolean monotonic)
Creates an instance of the discernability metric.
|
static Metric<ILSingleDimensional> |
__MetricV2.createDiscernabilityMetric(boolean monotonic,
double numTuples)
Creates an instance of the discernability metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createEntropyMetric()
Creates an instance of the non-monotonic non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createEntropyMetric(boolean monotonic)
Creates an instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createEntropyMetric(boolean monotonic,
double gsFactor)
Creates an instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createEntropyMetric(boolean monotonic,
double[][] cache,
int[][][] cardinalities,
int[][][] hierarchies)
Creates an instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createEntropyMetric(boolean monotonic,
double gsFactor,
Metric.AggregateFunction function)
Creates an instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createEntropyMetric(boolean monotonic,
Metric.AggregateFunction function)
Creates an instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createEntropyMetric(double gsFactor)
Creates an instance of the non-monotonic non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createHeightMetric()
Creates an instance of the height metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createHeightMetric(int minHeight,
int maxHeight)
Creates an instance of the height metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createHeightMetric(Metric.AggregateFunction function)
Creates an instance of the height metric.
|
static Metric<ILSingleDimensional> |
__MetricV2.createKLDivergenceMetric()
Creates an instance of the KL Divergence metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createLossMetric()
Creates an instance of the loss metric which treats generalization and suppression equally.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createLossMetric(double gsFactor)
Creates an instance of the loss metric with factors for weighting generalization and suppression.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createLossMetric(double gsFactor,
Metric.AggregateFunction function)
Creates an instance of the loss metric with factors for weighting generalization and suppression.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createLossMetric(Metric.AggregateFunction function)
Creates an instance of the loss metric which treats generalization and suppression equally.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createNormalizedEntropyMetric()
Creates an instance of the normalized entropy metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createNormalizedEntropyMetric(Metric.AggregateFunction function)
Creates an instance of the normalized entropy metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecisionMetric()
Creates an instance of the non-monotonic precision metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecisionMetric(boolean monotonic)
Creates an instance of the precision metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecisionMetric(boolean monotonic,
double gsFactor)
Creates an instance of the precision metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecisionMetric(boolean monotonic,
double gsFactor,
Metric.AggregateFunction function)
Creates an instance of the precision metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecisionMetric(boolean monotonic,
int[] heights,
double cells)
Creates an instance of the precision metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecisionMetric(boolean monotonic,
Metric.AggregateFunction function)
Creates an instance of the precision metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecisionMetric(double gsFactor)
Creates an instance of the non-monotonic precision metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecisionMetric(double gsFactor,
Metric.AggregateFunction function)
Creates an instance of the non-monotonic precision metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecisionMetric(Metric.AggregateFunction function)
Creates an instance of the non-monotonic precision metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecomputedEntropyMetric(double threshold)
Creates a potentially precomputed instance of the non-monotonic non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecomputedEntropyMetric(double threshold,
boolean monotonic)
Creates a potentially precomputed instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecomputedEntropyMetric(double threshold,
boolean monotonic,
double gsFactor)
Creates a potentially precomputed instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecomputedEntropyMetric(double threshold,
boolean monotonic,
double gsFactor,
Metric.AggregateFunction function)
Creates a potentially precomputed instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecomputedEntropyMetric(double threshold,
boolean monotonic,
Metric.AggregateFunction function)
Creates a potentially precomputed instance of the non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecomputedEntropyMetric(double threshold,
double gsFactor)
Creates a potentially precomputed instance of the non-monotonic non-uniform entropy metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecomputedLossMetric(double threshold)
Creates a potentially precomputed instance of the loss metric which treats generalization
and suppression equally.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecomputedLossMetric(double threshold,
double gsFactor)
Creates a potentially precomputed instance of the loss metric with factors for weighting generalization and suppression.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecomputedLossMetric(double threshold,
double gsFactor,
Metric.AggregateFunction function)
Creates a potentially precomputed instance of the loss metric with factors for weighting generalization and suppression.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecomputedLossMetric(double threshold,
Metric.AggregateFunction function)
Creates a potentially precomputed instance of the loss metric which treats generalization and suppression equally.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecomputedNormalizedEntropyMetric(double threshold)
Creates a potentially precomputed instance of the normalized entropy metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecomputedNormalizedEntropyMetric(double threshold,
Metric.AggregateFunction function)
Creates a potentially precomputed instance of the normalized entropy metric.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createStaticMetric(java.util.Map<java.lang.String,java.util.List<java.lang.Double>> loss)
Creates an instance of a metric with statically defined information loss.
|
static Metric<AbstractILMultiDimensional> |
__MetricV2.createStaticMetric(java.util.Map<java.lang.String,java.util.List<java.lang.Double>> loss,
Metric.AggregateFunction function)
Creates an instance of a metric with statically defined information loss.
|
Modifier and Type | Field and Description |
---|---|
Metric<?> |
AbstractTestUtilityMetricsPrecomputation.ARXUtilityMetricsTestCase.m1
First model
|
Metric<?> |
AbstractTestUtilityMetricsPrecomputation.ARXUtilityMetricsTestCase.m2
Second model
|
Constructor and Description |
---|
ARXUtilityMetricsTestCase(ARXConfiguration config,
java.lang.String sensitiveAttribute,
java.lang.String dataset,
Metric<?> m1,
Metric<?> m2)
Creates a new instance.
|
ARXUtilityMetricsTestCase(ARXConfiguration config,
java.lang.String sensitiveAttribute,
java.lang.String dataset,
Metric<?> m1,
Metric<?> m2)
Creates a new instance.
|