| Interface | Description |
|---|---|
| DomainShare |
Base interface for domain shares.
|
| Class | Description |
|---|---|
| __MetricV2 |
This internal class provides access to version 2 of all metrics.
|
| 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.
|
| AbstractMetricSingleDimensional |
This class provides an abstract skeleton for the implementation of single-dimensional metrics.
|
| Cardinalities |
This class represents cardinalities.
|
| DomainShareInterval<T> |
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.
|
| ILMultiDimensionalArithmeticMean |
This class implements an information loss which can be represented as a
decimal number per quasi-identifier.
|
| 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.
|
| ILSingleDimensionalWithBound |
Information loss with a potential lower bound.
|
| IO |
This class implements serialization for maps
|
| MetricMDHeight |
This class provides an implementation of the Height metric.
|
| MetricMDNMLoss |
This class implements a variant of the Loss metric.
|
| MetricMDNMLossPotentiallyPrecomputed |
This class implements a variant of the Loss metric.
|
| 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.
|
| MetricMDNUNMNormalizedEntropyPotentiallyPrecomputed |
This class provides an implementation of normalized non-uniform entropy.
|
| 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.
|
| MetricSDClassification |
This class provides an implementation of the classification metric.
|
| MetricSDDiscernability |
This class provides an implementation of the monotonic DM* metric.
|
| MetricSDNMAmbiguity |
This class implements a variant of the Ambiguity metric.
|
| 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. |
| 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
|