Package | Description |
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org.deidentifier.arx |
This package provides the public API for the ARX anonymization framework.
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org.deidentifier.arx.gui.model | |
org.deidentifier.arx.metric |
This package implements several metrics for information loss.
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org.deidentifier.arx.metric.v2 |
Modifier and Type | Method and Description |
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Metric<?> |
ARXConfiguration.getMetric()
Returns the metric used for measuring information loss.
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Metric<?> |
ARXConfiguration.ARXConfigurationInternal.getMetric()
Returns the metric used for measuring information loss.
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Modifier and Type | Method and Description |
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static ARXConfiguration |
ARXConfiguration.create(double suppressionLimit,
Metric<?> metric)
Creates a new config that allows the given percentage of outliers and
thus implements tuple suppression.
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static ARXConfiguration |
ARXConfiguration.create(Metric<?> metric)
Creates a new config that allows to define the metric for measuring information loss.
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void |
ARXLattice.Access.setMetric(Metric<?> metric) |
void |
ARXConfiguration.setMetric(Metric<?> metric)
Sets the utility metric for measuring information loss .
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Constructor and Description |
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ARXResult(DataHandle handle,
DataDefinition definition,
ARXLattice lattice,
int historySize,
double snapshotSizeSnapshot,
double snapshotSizeDataset,
Metric<?> metric,
ARXConfiguration config,
ARXLattice.ARXNode optimum,
long time)
Internal constructor for deserialization.
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Modifier and Type | Method and Description |
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Metric<?> |
ModelConfiguration.getMetric()
Delegates to an instance of ARXConfiguration.
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Modifier and Type | Method and Description |
---|---|
void |
ModelConfiguration.setMetric(Metric<?> metric)
Delegates to an instance of ARXConfiguration.
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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.
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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.
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static Metric<ILSingleDimensional> |
Metric.createDiscernabilityMetric()
Creates an instance of the discernability metric.
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static Metric<ILSingleDimensional> |
Metric.createDiscernabilityMetric(boolean monotonic)
Creates an instance of the discernability metric.
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static Metric<AbstractILMultiDimensional> |
Metric.createEntropyMetric()
Creates an instance of the non-monotonic non-uniform entropy metric.
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static Metric<AbstractILMultiDimensional> |
Metric.createEntropyMetric(boolean monotonic)
Creates an instance of the non-uniform entropy metric.
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static Metric<AbstractILMultiDimensional> |
Metric.createEntropyMetric(boolean monotonic,
Metric.AggregateFunction function)
Creates an instance of the non-uniform entropy metric.
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static Metric<AbstractILMultiDimensional> |
Metric.createHeightMetric()
Creates an instance of the height metric.
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static Metric<AbstractILMultiDimensional> |
Metric.createHeightMetric(Metric.AggregateFunction function)
Creates an instance of the height metric.
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abstract Metric<?> |
MetricDescription.createInstance(MetricConfiguration config)
Creates an instance with the given configuration options.
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static Metric<AbstractILMultiDimensional> |
Metric.createLossMetric()
Creates an instance of the loss metric which treats generalization and suppression equally.
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static Metric<AbstractILMultiDimensional> |
Metric.createLossMetric(double gsFactor)
Creates an instance of the loss metric with factors for weighting generalization and suppression.
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static Metric<AbstractILMultiDimensional> |
Metric.createLossMetric(double gsFactor,
Metric.AggregateFunction function)
Creates an instance of the loss metric with factors for weighting generalization and suppression.
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static Metric<AbstractILMultiDimensional> |
Metric.createLossMetric(Metric.AggregateFunction function)
Creates an instance of the loss metric which treats generalization and suppression equally.
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static Metric<?> |
Metric.createMetric(Metric<?> metric,
int minLevel,
int maxLevel)
This method supports backwards compatibility.
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static Metric<AbstractILMultiDimensional> |
Metric.createPrecisionMetric()
Creates an instance of the non-monotonic precision metric.
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static Metric<AbstractILMultiDimensional> |
Metric.createPrecisionMetric(boolean monotonic)
Creates an instance of the precision metric.
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static Metric<AbstractILMultiDimensional> |
Metric.createPrecisionMetric(boolean monotonic,
Metric.AggregateFunction function)
Creates an instance of the precision metric.
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static Metric<AbstractILMultiDimensional> |
Metric.createPrecisionMetric(Metric.AggregateFunction function)
Creates an instance of the non-monotonic precision metric.
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static Metric<AbstractILMultiDimensional> |
Metric.createPrecomputedEntropyMetric(double threshold)
Creates a potentially precomputed instance of the non-monotonic non-uniform entropy metric.
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static Metric<AbstractILMultiDimensional> |
Metric.createPrecomputedEntropyMetric(double threshold,
boolean monotonic)
Creates a potentially precomputed instance of the non-uniform entropy metric.
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static Metric<AbstractILMultiDimensional> |
Metric.createPrecomputedEntropyMetric(double threshold,
boolean monotonic,
Metric.AggregateFunction function)
Creates a potentially precomputed instance of the non-uniform entropy metric.
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static Metric<AbstractILMultiDimensional> |
Metric.createPrecomputedLossMetric(double threshold)
Creates a potentially precomputed instance of the loss metric which treats generalization
and suppression equally.
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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.
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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.
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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.
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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.
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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.
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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.
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static Metric<?> |
Metric.createMetric(Metric<?> metric,
int minLevel,
int maxLevel)
This method supports backwards compatibility.
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protected static MetricDescription |
Metric.getDescription(Metric<?> metric)
Returns a description for the given metric, if there is any, null otherwise.
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abstract boolean |
MetricDescription.isInstance(Metric<?> metric)
Returns whether the given metric is an instance of this description.
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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 |
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 |
MetricSDDiscernability
This class provides an implementation of the monotonic DM* metric.
|
class |
MetricSDNMDiscernability
This class provides an implementation of the non-monotonic DM metric.
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Modifier and Type | Method and Description |
---|---|
static Metric<ILSingleDimensional> |
__MetricV2.createAECSMetric()
Creates a new instance of the AECS metric.
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static Metric<ILSingleDimensional> |
__MetricV2.createAECSMetric(double rowCount)
Creates a new instance of the AECS metric.
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static Metric<ILSingleDimensional> |
__MetricV2.createDiscernabilityMetric()
Creates an instance of the discernability metric.
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static Metric<ILSingleDimensional> |
__MetricV2.createDiscernabilityMetric(boolean monotonic)
Creates an instance of the discernability metric.
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static Metric<ILSingleDimensional> |
__MetricV2.createDiscernabilityMetric(boolean monotonic,
double numTuples)
Creates an instance of the discernability metric.
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static Metric<AbstractILMultiDimensional> |
__MetricV2.createEntropyMetric()
Creates an instance of the non-monotonic non-uniform entropy metric.
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static Metric<AbstractILMultiDimensional> |
__MetricV2.createEntropyMetric(boolean monotonic)
Creates an instance of the non-uniform entropy metric.
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static Metric<AbstractILMultiDimensional> |
__MetricV2.createEntropyMetric(boolean monotonic,
double[][] cache,
int[][][] cardinalities,
int[][][] hierarchies)
Creates an instance of the non-uniform entropy metric.
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static Metric<AbstractILMultiDimensional> |
__MetricV2.createEntropyMetric(boolean monotonic,
Metric.AggregateFunction function)
Creates an instance of the non-uniform entropy metric.
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static Metric<AbstractILMultiDimensional> |
__MetricV2.createHeightMetric()
Creates an instance of the height metric.
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static Metric<AbstractILMultiDimensional> |
__MetricV2.createHeightMetric(int minHeight,
int maxHeight)
Creates an instance of the height metric.
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static Metric<AbstractILMultiDimensional> |
__MetricV2.createHeightMetric(Metric.AggregateFunction function)
Creates an instance of the height metric.
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static Metric<AbstractILMultiDimensional> |
__MetricV2.createLossMetric()
Creates an instance of the loss metric which treats generalization and suppression equally.
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static Metric<AbstractILMultiDimensional> |
__MetricV2.createLossMetric(double gsFactor)
Creates an instance of the loss metric with factors for weighting generalization and suppression.
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static Metric<AbstractILMultiDimensional> |
__MetricV2.createLossMetric(double gsFactor,
Metric.AggregateFunction function)
Creates an instance of the loss metric with factors for weighting generalization and suppression.
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static Metric<AbstractILMultiDimensional> |
__MetricV2.createLossMetric(Metric.AggregateFunction function)
Creates an instance of the loss metric which treats generalization and suppression equally.
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static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecisionMetric()
Creates an instance of the non-monotonic precision metric.
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static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecisionMetric(boolean monotonic)
Creates an instance of the precision metric.
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static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecisionMetric(boolean monotonic,
int[] heights,
double cells)
Creates an instance of the precision metric.
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static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecisionMetric(boolean monotonic,
Metric.AggregateFunction function)
Creates an instance of the precision metric.
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static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecisionMetric(Metric.AggregateFunction function)
Creates an instance of the non-monotonic precision metric.
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static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecomputedEntropyMetric(double threshold)
Creates a potentially precomputed instance of the non-monotonic non-uniform entropy metric.
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static Metric<AbstractILMultiDimensional> |
__MetricV2.createPrecomputedEntropyMetric(double threshold,
boolean monotonic)
Creates a potentially precomputed instance of the non-uniform entropy metric.
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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.createPrecomputedLossMetric(double threshold)
Creates a potentially precomputed instance of the loss metric which treats generalization
and suppression equally.
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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.
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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.
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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.
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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.
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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.
|