Package org.deidentifier.arx.metric.v2
package org.deidentifier.arx.metric.v2
Main package implementing quality models
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KlasseBeschreibungThis internal class provides access to version 2 of all metrics.This class implements an information loss which can be represented as a decimal number per quasi-identifier.This class implements an information loss which can be represented as a decimal number per quasi-identifier.This class provides an abstract skeleton for the implementation of multi-dimensional metrics.This class provides an abstract skeleton for the implementation of metrics that can either be precomputed or not.This class provides an abstract skeleton for the implementation of single-dimensional metrics.This class represents cardinalities.Base interface for domain shares.This class represents a set of domain shares for an attribute.This class represents a set of domain shares for an attribute.This class represents a set of domain shares for an attribute.This class represents a reliable set of domain shares for an attribute.This class implements an information loss which can be represented as a decimal number per quasi-identifier.This class implements an information loss which can be represented as a decimal number per quasi-identifier.This class implements an information loss which can be represented as a decimal number per quasi-identifier.This class implements an information loss which can be represented as a decimal number per quasi-identifier.This class implements an information loss which can be represented as a decimal number per quasi-identifier.Information loss with a potential lower bound.This class implements information loss using score values for data-independent differential privacy with appropriate comparison semantics (i.e. higher score values are better).This class implements an information loss which can be represented as a single decimal number.Information loss with a potential lower bound.This class implements serialization for mapsThis class provides an implementation of the Height metric.This class implements a variant of the Loss metric.This class implements a variant of the Loss metric.This class implements a variant of the Loss metric.This class provides an implementation of a weighted precision metric as proposed in:
Sweeney, L. (2002).This class provides an implementation of the non-uniform entropy metric.This class provides an implementation of the non-uniform entropy metric.This class provides an efficient implementation of the non-uniform entropy metric.This class provides an implementation of the non-uniform entropy metric.This class provides an implementation of the non-uniform entropy metric.This class provides an implementation of the non-uniform entropy metric.This class provides an implementation of normalized non-uniform entropy.This class provides an implementation of normalized non-uniform entropy.This class provides an efficient implementation of normalized non-uniform entropy.This class provides an implementation of a weighted precision metric as proposed in:
Sweeney, L. (2002).This class provides an implementation of a static metric in which information loss is user-defined per generalization level.This class provides an implementation of the (normalized) average equivalence class size metric.This class provides an implementation of the classification metric.This class provides an implementation of the monotonic DM* metric.This class implements a variant of the Ambiguity metric.This class provides an implementation of the non-monotonic DM metric.This class implements a the entropy-based information loss model proposed in:
A Game Theoretic Framework for Analyzing Re-Identification Risk.This class implements the KL Divergence metric.This class implements a model which maximizes publisher benefit according to the model proposed in:
A Game Theoretic Framework for Analyzing Re-Identification Risk.A class encapsulating information about data qualityA class that supports associating input with output