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.framework.lattice |
This package provides the internal generalization lattice
|
org.deidentifier.arx.metric |
Package providing access to quality models
|
org.deidentifier.arx.metric.v2 |
Main package implementing quality models
|
Modifier and Type | Method and Description |
---|---|
InformationLoss<?> |
ARXLattice.getHighestScore()
Returns the highest score.
|
InformationLoss<?> |
ARXLattice.ARXNode.getHighestScore()
Returns the highest score.
|
InformationLoss<?> |
ARXLattice.getLowestScore()
Returns the lowest score.
|
InformationLoss<?> |
ARXLattice.ARXNode.getLowestScore()
Returns the highest score.
|
InformationLoss<?> |
ARXProcessStatistics.Step.getScore()
Returns a node's lower bound, if any.
|
Modifier and Type | Method and Description |
---|---|
void |
ARXLattice.ARXNode.Access.setHighestScore(InformationLoss<?> a)
Sets the maximal information loss.
|
void |
ARXLattice.ARXNode.Access.setLowerBound(InformationLoss<?> a)
Sets the lower bound.
|
void |
ARXLattice.ARXNode.Access.setLowestScore(InformationLoss<?> a)
Sets the minimal information loss.
|
Modifier and Type | Field and Description |
---|---|
InformationLoss<?> |
TransformationResult.informationLoss
Information loss.
|
InformationLoss<?> |
TransformationResult.lowerBound
Lower bound.
|
Modifier and Type | Method and Description |
---|---|
InformationLoss<?> |
Transformation.getInformationLoss()
Returns the information loss
|
InformationLoss<?> |
Transformation.getLowerBound()
Returns the lower bound on information loss
|
InformationLoss<?> |
SolutionSpaceLong.getUtility(java.lang.Object _identifier)
Returns the utility of the transformation with the given identifier
|
InformationLoss<?> |
SolutionSpaceIntArray.getUtility(java.lang.Object identifier)
Returns the utility of the transformation with the given identifier
|
abstract InformationLoss<?> |
SolutionSpace.getUtility(java.lang.Object _identifier)
Returns the utility of the transformation with the given identifier
|
Modifier and Type | Method and Description |
---|---|
void |
Transformation.setInformationLoss(InformationLoss<?> informationLoss)
Sets the information loss
|
void |
Transformation.setLowerBound(InformationLoss<?> lowerBound)
Sets the lower bound
|
Modifier and Type | Class and Description |
---|---|
class |
InformationLossWithBound<T extends InformationLoss<?>>
Information loss with a potential lower bound.
|
class |
Metric<T extends InformationLoss<?>>
Abstract base class for metrics.
|
class |
MetricWeighted<T extends InformationLoss<?>>
This class provides an abstract skeleton for the implementation of weighted metrics.
|
Modifier and Type | Method and Description |
---|---|
abstract int |
InformationLoss.compareTo(InformationLoss<?> other)
Compares the loss to the other.
|
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.
|
abstract void |
InformationLoss.max(InformationLoss<?> other)
Retains the maximum of this and other.
|
abstract void |
InformationLoss.min(InformationLoss<?> other)
Retains the minimum of this and other.
|
abstract double |
InformationLoss.relativeTo(InformationLoss<?> min,
InformationLoss<?> max)
Returns the value relative to the other instance.
|
abstract double |
InformationLoss.relativeTo(InformationLoss<?> min,
InformationLoss<?> max)
Returns the value relative to the other instance.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractILMultiDimensional
This class implements an information loss which can be represented as a
decimal number per quasi-identifier.
|
class |
AbstractILMultiDimensionalReduced
This class implements an information loss which can be represented as a
decimal number per quasi-identifier.
|
class |
ILMultiDimensionalArithmeticMean
This class implements an information loss which can be represented as a
decimal number per quasi-identifier.
|
class |
ILMultiDimensionalGeometricMean
This class implements an information loss which can be represented as a
decimal number per quasi-identifier.
|
class |
ILMultiDimensionalMax
This class implements an information loss which can be represented as a
decimal number per quasi-identifier.
|
class |
ILMultiDimensionalRank
This class implements an information loss which can be represented as a
decimal number per quasi-identifier.
|
class |
ILMultiDimensionalSum
This class implements an information loss which can be represented as a
decimal number per quasi-identifier.
|
class |
ILScore
This class implements information loss using score values for data-independent
differential privacy with appropriate comparison semantics
(i.e. higher score values are better).
|
class |
ILSingleDimensional
This class implements an information loss which can be represented as a
single decimal number.
|
Modifier and Type | Method and Description |
---|---|
InformationLoss<java.lang.Double> |
ILSingleDimensional.clone() |
InformationLoss<org.apache.commons.math3.fraction.BigFraction> |
ILScore.clone() |
InformationLoss<double[]> |
ILMultiDimensionalSum.clone() |
InformationLoss<double[]> |
ILMultiDimensionalRank.clone() |
InformationLoss<double[]> |
ILMultiDimensionalMax.clone() |
InformationLoss<double[]> |
ILMultiDimensionalGeometricMean.clone() |
InformationLoss<double[]> |
ILMultiDimensionalArithmeticMean.clone() |
abstract InformationLoss<double[]> |
AbstractILMultiDimensionalReduced.clone()
Override this to implement a variant.
|
abstract InformationLoss<double[]> |
AbstractILMultiDimensional.clone() |
static InformationLoss<?> |
__MetricV2.createILMultiDimensionalArithmeticMean(double value)
Helper method.
|
static InformationLoss<?> |
__MetricV2.createILMultiDimensionalSum(double value)
Helper method.
|
static InformationLoss<?> |
__MetricV2.createILSingleDimensional(double value)
Helper method.
|
InformationLoss<?> |
AbstractMetricSingleDimensional.createMaxInformationLoss() |
InformationLoss<?> |
AbstractMetricMultiDimensionalPotentiallyPrecomputed.createMaxInformationLoss() |
InformationLoss<?> |
AbstractMetricMultiDimensional.createMaxInformationLoss() |
InformationLoss<?> |
AbstractMetricSingleDimensional.createMinInformationLoss() |
InformationLoss<?> |
AbstractMetricMultiDimensionalPotentiallyPrecomputed.createMinInformationLoss() |
InformationLoss<?> |
AbstractMetricMultiDimensional.createMinInformationLoss() |
Modifier and Type | Method and Description |
---|---|
int |
ILSingleDimensional.compareTo(InformationLoss<?> other) |
int |
ILScore.compareTo(InformationLoss<?> other) |
int |
ILMultiDimensionalRank.compareTo(InformationLoss<?> other) |
int |
AbstractILMultiDimensionalReduced.compareTo(InformationLoss<?> other) |
abstract int |
AbstractILMultiDimensional.compareTo(InformationLoss<?> other) |
void |
ILSingleDimensional.max(InformationLoss<?> other) |
void |
ILScore.max(InformationLoss<?> other) |
void |
AbstractILMultiDimensional.max(InformationLoss<?> other) |
void |
ILSingleDimensional.min(InformationLoss<?> other) |
void |
ILScore.min(InformationLoss<?> other) |
void |
AbstractILMultiDimensional.min(InformationLoss<?> other) |
double |
ILSingleDimensional.relativeTo(InformationLoss<?> min,
InformationLoss<?> max) |
double |
ILSingleDimensional.relativeTo(InformationLoss<?> min,
InformationLoss<?> max) |
double |
ILScore.relativeTo(InformationLoss<?> min,
InformationLoss<?> max) |
double |
ILScore.relativeTo(InformationLoss<?> min,
InformationLoss<?> max) |
double |
ILMultiDimensionalRank.relativeTo(InformationLoss<?> min,
InformationLoss<?> max) |
double |
ILMultiDimensionalRank.relativeTo(InformationLoss<?> min,
InformationLoss<?> max) |
double |
AbstractILMultiDimensionalReduced.relativeTo(InformationLoss<?> min,
InformationLoss<?> max) |
double |
AbstractILMultiDimensionalReduced.relativeTo(InformationLoss<?> min,
InformationLoss<?> max) |
abstract double |
AbstractILMultiDimensional.relativeTo(InformationLoss<?> min,
InformationLoss<?> max) |
abstract double |
AbstractILMultiDimensional.relativeTo(InformationLoss<?> min,
InformationLoss<?> max) |