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.framework.data |
This package implements data management, i.e., encoding and representing input data, output data and generalization
hierarchies
|
org.deidentifier.arx.metric |
Package providing access to quality models
|
org.deidentifier.arx.metric.v2 |
Main package implementing quality models
|
Class and Description |
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QualityMetadata
A class encapsulating information about data quality
|
Class and Description |
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DomainShare
Base interface for domain shares.
|
DomainShareReliable
This class represents a reliable set of domain shares for an attribute.
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Class and Description |
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AbstractILMultiDimensional
This class implements an information loss which can be represented as a
decimal number per quasi-identifier.
|
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).
|
ILSingleDimensional
This class implements an information loss which can be represented as a
single decimal number.
|
MetricSDNMEntropyBasedInformationLoss
This class implements a the entropy-based information loss model proposed in:
A Game Theoretic Framework for Analyzing Re-Identification Risk. |
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. |
QualityMetadata
A class encapsulating information about data quality
|
Class and Description |
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AbstractILMultiDimensional
This class implements an information loss which can be represented as a
decimal number per quasi-identifier.
|
AbstractILMultiDimensionalReduced
This class implements an information loss which can be represented as a
decimal number per quasi-identifier.
|
AbstractMetricMultiDimensional
This class provides an abstract skeleton for the implementation of multi-dimensional metrics.
|
AbstractMetricMultiDimensionalPotentiallyPrecomputed
This class provides an abstract skeleton for the implementation of metrics
that can either be precomputed or not.
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AbstractMetricSingleDimensional
This class provides an abstract skeleton for the implementation of single-dimensional metrics.
|
DomainShare
Base interface for domain shares.
|
DomainShareInterval
This class represents a set of domain shares for an attribute.
|
DomainShareMaterialized
This class represents a set of domain shares for an attribute.
|
DomainShareRedaction
This class represents a set of domain shares for an attribute.
|
DomainShareReliable
This class represents a reliable set of domain shares for an attribute.
|
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).
|
ILSingleDimensional
This class implements an information loss which can be represented as a
single decimal number.
|
ILSingleDimensionalWithBound
Information loss with a potential lower bound.
|
MetricMDNMLoss
This class implements a variant of the Loss metric.
|
MetricMDNMPrecision
This class provides an implementation of a weighted precision metric as
proposed in:
Sweeney, L. (2002). |
MetricMDNUEntropyPrecomputed
This class provides an efficient implementation of the non-uniform entropy
metric.
|
MetricMDNUNMEntropyPrecomputed
This class provides an implementation of the non-uniform entropy
metric.
|
MetricMDNUNMNormalizedEntropyPrecomputed
This class provides an efficient implementation of normalized non-uniform entropy.
|
MetricSDNMDiscernability
This class provides an implementation of the non-monotonic DM metric.
|
MetricSDNMEntropyBasedInformationLoss
This class implements a the entropy-based information loss model proposed in:
A Game Theoretic Framework for Analyzing Re-Identification Risk. |
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. |