Interface | Description |
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DomainShare |
Base interface for domain shares.
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Class | Description |
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__MetricV2 |
This internal class provides access to version 2 of all metrics.
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AbstractILMultiDimensional |
This class implements an information loss which can be represented as a
decimal number per quasi-identifier.
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AbstractILMultiDimensionalReduced |
This class implements an information loss which can be represented as a
decimal number per quasi-identifier.
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AbstractMetricMultiDimensional |
This class provides an abstract skeleton for the implementation of multi-dimensional metrics.
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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.
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Cardinalities |
This class represents cardinalities.
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DomainShareInterval<T> |
This class represents a set of domain shares for an attribute.
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DomainShareMaterialized |
This class represents a set of domain shares for an attribute.
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DomainShareRedaction |
This class represents a set of domain shares for an attribute.
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DomainShareReliable |
This class represents a reliable set of domain shares for an attribute.
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ILMultiDimensionalArithmeticMean |
This class implements an information loss which can be represented as a
decimal number per quasi-identifier.
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ILMultiDimensionalGeometricMean |
This class implements an information loss which can be represented as a
decimal number per quasi-identifier.
|
ILMultiDimensionalMax |
This class implements an information loss which can be represented as a
decimal number per quasi-identifier.
|
ILMultiDimensionalRank |
This class implements an information loss which can be represented as a
decimal number per quasi-identifier.
|
ILMultiDimensionalSum |
This class implements an information loss which can be represented as a
decimal number per quasi-identifier.
|
ILMultiDimensionalWithBound |
Information loss with a potential lower bound.
|
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.
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ILSingleDimensionalWithBound |
Information loss with a potential lower bound.
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IO |
This class implements serialization for maps
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MetricMDHeight |
This class provides an implementation of the Height metric.
|
MetricMDNMLoss |
This class implements a variant of the Loss metric.
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MetricMDNMLossPotentiallyPrecomputed |
This class implements a variant of the Loss metric.
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MetricMDNMLossPrecomputed |
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). |
MetricMDNUEntropy |
This class provides an implementation of the non-uniform entropy
metric.
|
MetricMDNUEntropyPotentiallyPrecomputed |
This class provides an implementation of the non-uniform entropy
metric.
|
MetricMDNUEntropyPrecomputed |
This class provides an efficient implementation of the non-uniform entropy
metric.
|
MetricMDNUNMEntropy |
This class provides an implementation of the non-uniform entropy
metric.
|
MetricMDNUNMEntropyPotentiallyPrecomputed |
This class provides an implementation of the non-uniform entropy
metric.
|
MetricMDNUNMEntropyPrecomputed |
This class provides an implementation of the non-uniform entropy
metric.
|
MetricMDNUNMNormalizedEntropy |
This class provides an implementation of normalized non-uniform entropy.
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MetricMDNUNMNormalizedEntropyPotentiallyPrecomputed |
This class provides an implementation of normalized non-uniform entropy.
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MetricMDNUNMNormalizedEntropyPrecomputed |
This class provides an efficient implementation of normalized non-uniform entropy.
|
MetricMDPrecision |
This class provides an implementation of a weighted precision metric as
proposed in:
Sweeney, L. (2002). |
MetricMDStatic |
This class provides an implementation of a static metric in
which information loss is user-defined per generalization level.
|
MetricSDAECS |
This class provides an implementation of the (normalized) average equivalence class size metric.
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MetricSDClassification |
This class provides an implementation of the classification metric.
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MetricSDDiscernability |
This class provides an implementation of the monotonic DM* metric.
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MetricSDNMAmbiguity |
This class implements a variant of the Ambiguity metric.
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MetricSDNMDiscernability |
This class provides an implementation of the non-monotonic DM metric.
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MetricSDNMEntropyBasedInformationLoss |
This class implements a the entropy-based information loss model proposed in:
A Game Theoretic Framework for Analyzing Re-Identification Risk. |
MetricSDNMKLDivergence |
This class implements the KL Divergence metric.
|
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<T> |
A class encapsulating information about data quality
|
TupleMatcher |
A class that supports associating input with output
|