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Klasse
Beschreibung
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.
Abstract base class for menus and toolbars
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 abstract class represents an aggregate function.
A builder for aggregate functions.
An aggregate function that has a parameter.
An aggregate function that returns the arithmetic mean, if it may be computed, "NULL"
otherwise.
An aggregate function that returns the arithmetic mean of min Ungültige Eingabe: "&" max, if it may be computed, "NULL"
otherwise.
An aggregate function that returns an interval consisting of the
first and the last element following the predefined order .
An aggregate function that returns a common prefix.
An aggregate function that returns a constant value.
An aggregate function that returns the geometric mean, if it may be computed, "NULL"
otherwise.
An aggregate function that returns the geometric mean of min Ungültige Eingabe: "&" max, if it may be computed, "NULL"
otherwise.
An aggregate function that returns an interval [min, max] .
An aggregate function that returns a set of all data values.
An aggregate function that returns a set of the prefixes of the data values.
A generic interface for analyses that are performed asynchronously.
This class implements a base class for views that show statistic properties of the data.
The current context.
The current context.
The current context.
The current context.
The current context.
The current context.
This class implements a context for visualizing statistics.
This class manages the execution of asynchronous analyses.
This class offers several methods to define parameters and execute the ARX
algorithm.
A PDF document
An base class for configuration classes for classification experiments
A generic configuration for the ARX anonymizer.
The algorithms supported by ARX
Class for internal use that provides access to more parameters and functionality.
Monotonicity.
The semantics of heuristic search steps.
Basic configuration of monetary amounts, such as the publisher's benefit
per record or the per-record fine fine for a successful re-identification attack.
Configuration for feature scaling
This class implements a representation of the generalization lattice that is
exposed to users of the API.
Reflects different anonymity properties.
Context for deserialization.
This class implements a listener for the ARX framework.
This class models population properties for risk estimation
Regions
Statistics about the anonymization process for output data
One individual anonymization step
Encapsulates the results of an execution of the ARX algorithm.
Runtime configuration for the solver
This class implements an abstract base class for wizards.
A custom implementation of the default SWT WizardDialog that is more
compact and allows adding additional buttons.
A specification for a button to add to the wizard.
Represents an attribute type.
This class implements a generalization hierarchy.
The default implementation of a generalization hierarchy.
This class is used to define aggregate functions for microaggregation.
This class describes a microaggregation function
This criterion ensures that an estimate for the average re-identification risk falls
below a given threshold.
This class handles compatibility issues with object deserialization
Basic-beta-Likeness:
Jianneng Cao, Panagiotis Karras:
Publishing Microdata with a Robust Privacy Guarantee
VLDB 2012.
Jianneng Cao, Panagiotis Karras:
Publishing Microdata with a Robust Privacy Guarantee
VLDB 2012.
This class represents cardinalities.
Style information for a PDF document
Enum for list styles
A class for managing charsets.
Configuration for logistic regression
Prior function for regularization
Configuration for naive bayes classification
Type of bayes classifier
Configuration for Random Forest classifiers
Split rule for the decision tree
A complete specification of all input and output data
Metadata about a single feature
Implements a classifier
A classification result
Supports interaction with the system clipboard.
Supports interaction with the system clipboard.
This component displays a data table.
This class implements a table, in which properties can be filtered.
This component allows to configure the coding model.
This class implements an editor for generalization hierarchies.
This class implements an a menu for the editor for generalization hierarchies.
The figure of Express Meter.
A stack layout for multiple columns
A layout that shows different contents, depending on available size
A risk monitor
A component for displaying risk profiles
A specific profile that can be added
A component for configuring risk thresholds
This class implements a wrapper around a control that displays the current status:
(1) nothing to display, (2) working, (3) done (shows the control).
A label that can display animated GIFs.
A progress provider
This wrapper around CTabFolder fixes SWT bug 507611 for ARX and
refresh issues with CTabFolders when changing selected items.
A virtual table implemented with NatTable.
This class implements a titled border.
This class implements a titled folder.
A basic title bar, which offers some buttons.
Instances of this class provide a separator with a title and/or an image.
Internal class for interrupts.
The main controller for the whole tool.
Provides methods for creating checksums CSV encoded data.
This class implements a reader for CSV encoded information.
Provides methods for writing CSV encoded data.
Reads a CSV encoded generalization hierarchy.
Additional options for reading/writing CSV files
Syntax for a CSV file.
Component table
Configuration for component data table
Table context wrapper
Data provider wrapper
Table layer transform wrapper
Table style wrapper
Represents input data for the ARX framework.
The default implementation of a data object.
Encapsulates a definition of the types of attributes contained in a dataset.
This class encapsulates a generalization scheme
A specific generalization degree
This class provides access to dictionary encoded data.
An implementation of the DataHandle interface for input data.
Wrapper class that provides information to StatisticsBuilder.
Interface
An implementation of the class DataHandle for output data.
This implementation of a data handle projects a given data handle onto a given research subset.
Wrapper for data provider
This class represents different scales of measure.
A selector for tuples.
This class provides configuration options for importing data from CSV-files, from Excel-files
or via a JDBC connection.
This class represents a the dataset that is to be de-identified
as a subset of the given population table.
Paints the cell background using an image.
A BodyLayerStack for the DataView.
A column style for the data view.
A label accumulator for the data view.
A context for the data view.
A data provider that handles missing values.
A table decorator.
Adds an additional column to fill up the space.
Style config for fill layouts.
A grid layer for the data view.
A grid layer stack for the data view.
A data provider based on a data handle.
Paints an image.
Resize column action that only fires for smaller datasets
A configuration for row headers in the data view.
A selection layer for data views.
This class provides access to the data types supported by the ARX framework.
Base class for date/time types.
Base class for numeric types.
Base class for numeric types.
Base class for ordered string types.
Base class for string types.
An entry in the list of available data types.
An interface for data types with format.
An interface for data types with a ratio scale.
Delta-disclosure privacy as proposed in:
Justin Brickell and Vitaly Shmatikov:
The Cost of Privacy: Destruction of Data-mining Utility in Anonymized Data Publishing
Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
2008
Justin Brickell and Vitaly Shmatikov:
The Cost of Privacy: Destruction of Data-mining Utility in Anonymized Data Publishing
Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
2008
Class for creating debug data.
Code based on: https://www.eclipse.org/articles/swt-design-2/sleak.htm
A listener that acts as a selection listener and a modify listener which defers change
events for a given amount of time
This class implements a data object that can be passed to the heatmap widget to
display the contingency table.
An about dialog.
A dialog for defining parameters of the anonymization method
A dialog for displaying the audit trail
This class implements a dialog for editing a classification configuration
A selection dialog for elements from a combo box.
A selection dialog for elements from a combo box.
A dialog for selecting privacy models.
A dialog for adding and configuring privacy models.
An about dialog.
A dialog to select default configurations for privacy models
A dialog for displaying error messages.
A dialog for finding and replacing data items
A dialog to select a generalization scheme
A help dialog.
Configuration for the help dialog.
An entry in the help dialog.
This class implements a dialog for selecting multiple elements
An dialog that allows ordering data items.
This class implements a dialog for creating a project.
This class implements a dialog for editing project properties.
Query dialog
A dialog for defining thresholds for top and bottom coding
The distinct l-diversity privacy criterion.
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.
The d-presence criterion
Published in:
Nergiz M, Atzori M, Clifton C.
(e,d)-Differential Privacy implemented with SafePub as proposed in:
Bild R, Kuhn KA, Prasser F.
Base class for editors
Implements a view on a b-likeness privacy model
Implements a view on a d-disclosure privacy criterion
A view on an (e,d)-DP criterion.
A view on a d-presence criterion.
A view on a k-anonymity criterion.
A view on a k-map criterion.
A view on an l-diversity criterion.
Editor for the profitability privacy model
A view on a risk-based criterion.
Implements a view on a t-closeness criterion.
String editor
An abstract element
Complex element of data items
PDF list
PDF text element
PDF page break
PDF subtitle element
PDF text element
Style
PDF title element
PDF page break
Enhanced-beta-Likeness:
Jianneng Cao, Panagiotis Karras:
Publishing Microdata with a Robust Privacy Guarantee
VLDB 2012.
Jianneng Cao, Panagiotis Karras:
Publishing Microdata with a Robust Privacy Guarantee
VLDB 2012.
The entropy l-diversity privacy model.
Enumerator of entropy estimators for the entropy-l-diversity privacy model.
The t-closeness criterion with equal-distance EMD.
A privacy criterion that is explicitly bound to a sensitive attribute.
An implementation of the exponential mechanism for discrete domains as proposed in:
McSherry, Frank, and Kunal Talwar:
Mechanism design via differential privacy.
A very basic map using golden ratio hashing and linear probing.
Wraps a writer.
Reset command
A gradient
A hash groupify operator.
Entry
The t-closeness criterion with hierarchical-distance EMD.
Base class for hierarchy builders.
The three types of builders.
This class enables building hierarchies for dates.
A format-class for localization
Granularity
This abstract base class enables building hierarchies for categorical and non-categorical values.
A group representation to be used by subclasses.
This class represents a fanout parameter.
This class represents a level in the hierarchy.
This class enables building hierarchies for non-categorical values by mapping them
into given intervals.
This class represents an interval.
For each direction, this class encapsulates three bounds.
This class enables building hierarchies for categorical and non-categorical values
by ordering the data items and merging into groups with predefined sizes.
A serializable comparator.
This class enables building hierarchies mostly for categorical variables
by iteratively removing the value with lowest priority
For priorities
This class enables building hierarchies for categorical and non-categorical values
using redaction.
Order
This class implements a wizard for generalization hierarchies.
Result of the wizard.
Updateable part of the wizard.
The general editor for hierarchies.
Editor for functions.
Tiny callback for parents.
Editor for groups.
An editor for intervals.
Layouts the tree shown in the wizard.
The editor's menu.
Editor for ranges.
Renders the content.
Base class for rendering contexts.
A rendering context for a group.
A rendering context for an interval.
The base model for the wizard.
An abstract base model for all builders.
A model for date-based builders.
A base-class for grouping-based builders, i.e., order-based and interval-based builders
This class represents a group.
This class represents an interval.
This class represents an adjustment.
A model for interval-based builders.
A model for order-based builders.
A model for priority-based builders.
Priorities
A model for redaction-based builders.
An abstract base class for pages that allow configuring a builder.
A page for configuring the date-based builder.
The final page that shows an overview of the resulting hierarchy.
A page for configuring the interval-based builder.
A page for configuring the order-based builder.
A page for configuring the priority-based builder.
A page for configuring the redaction-based builder.
A hierarchy page for choosing the type of builder.
Utility class providing access to important constants for finding HIPAA identifiers.
Utility class providing access to important constants for finding HIPAA identifiers.
Provides information about the occurrence of an HIPPA identifier
Represents the HIPPA identifiers
Represents the classifier for the HIPAA identifier.
Interface for components.
Interface for dialogs.
Interface for an editor for a given data type.
Interface to be implemented when columns can be referred to by an index.
An interface for layouts.
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.
A privacy criterion that is implicitly bound to the quasi-identifiers.
Base adapter for all data sources
This defines properties and methods that all data source import adapters have
in common.
Import adapter for CSV files
This adapter can import data from a CSV file.
Import adapter for Excel files
This adapter can import data from Excel files.
Import adapter for JDBC
This adapter can import data from JDBC sources.
Represents a single data column
This represents a single column that will be imported from.
Represents a single CSV data column
CSV columns are referred to by an index (see
ImportColumnIndexed).Represents a single Excel data column
Excel columns are referred to by an index (see
ImportColumnIndexed).Superclass for column types that are only referred to by an index.
Represents a single JDBC data column
JDBC columns can be referred to by both an index (
) and
by name (
Ungültige Referenz
IIndexColumnIImportColumnNamed.Abstract base configuration
This abstract superclass defines properties that all configurations have
in common, i.e. a notion of columns, which can be added and retrieved.
Configuration describing a CSV file.
Configuration describing an Excel file
This is used to describe Excel files.
Valid file types for Excel files
XLS is the "old" Excel file type, XLSX is the "new" Excel file type.
Configuration describing a file in general
File based configurations should extend this class as the notion of a
ImportConfigurationFile.fileLocation is common to all of them.Configuration describing a JDBC source.
Wizard guiding the user through the process of importing data
The user is taken through the process of importing data into the GUI step by
step.
Stores all of the data gathered by the wizard and offers means to access it
This object is accessible to all pages of the wizard and can be used to store
and/or retrieve data.
Possible sources for importing data from.
Wrapper for
ImportColumn used in the wizard context
This is a wrapper for ImportColumn.Column overview page
This pages gives the user an overview of the detected columns and allows him
to change things around.
CSV page
This page offers means to import data from a CSV file.
Excel page
This page offers means to import data from an Excel file.
JDBC page
This page offers means to specify connection details for a database.
Preview page
This page gives the user a preview over the data and how it is about to be
imported.
Source selection page
This page provides means to select the source the user wants to import data
from.
Table overview page
This pages gives the user an overview of the detected tables and allows him
to select the desired one by clicking on it.
This is a special criterion that does not enforce any privacy guarantees
but allows to define a data subset.
This class implements an abstract base class for information loss.
Information loss with a potential lower bound.
Information loss with a potential lower bound.
Interval arithmetic system
Arithmetic exception
A basic double interval
This class implements serialization for maps
Utility for I/O
An interface for validators.
An interface for views.
The k-anonymity criterion
Published in:
Sweeney L.
This class implements the k-map privacy model as proposed by Latanya Sweeney.
As an alternative to explicitly providing data about the underlying population, cell sizes can be can be estimated with the D3 (Poisson) and D4 (zero-truncated Poisson) estimators proposed in:
K.
As an alternative to explicitly providing data about the underlying population, cell sizes can be can be estimated with the D3 (Poisson) and D4 (zero-truncated Poisson) estimators proposed in:
K.
Estimators for cell sizes in the population.
The body layer.
Adds an additional column to fill up the space.
The column layer.
Adds additional rows at the end that fill up the available height.
The row layer.
A selection layer for table views.
Viewport layer
This view displays settings for all attributes.
This class implements the layout for the data definition perspective.
This class layouts the exploration view.
This layout manages views for criteria and the population model
This class layouts the risk analysis view.
Base class for layouts in this perspective
Layouts the risk analysis perspective.
Layouts the risk analysis perspective.
This layout manages views for general settings regarding data transformation.
This class layouts the analysis view.
Type of view which is displayed
Layouts the visualization and allows enabling/disabling them.
An abstract base class for l-diversity criteria
Published in:
Machanavajjhala A, Kifer D, Gehrke J.
Main entry point.
This class implements the global main menu.
This abstract class implements a menu group
This abstract class implements a generic menu item
This abstract class implements a menu separator
This class implements a splash window.
This class implements the global application tool bar.
This class implements the global application window.
Abstract base class for metrics.
Pluggable aggregate functions.
This class provides an implementation of the (normalized) average equivalence class size metric.
A class for a configuration of a metric.
This class provides an abstract skeleton for the implementation of metrics.
A class describing a metric and its configuration options.
This class provides an implementation of the DM metric (non-monotonic).
This class provides an implementation of the DM* metric (monotonic variant of
the Discernability Metric).
This class provides an efficient implementation of the non-uniform entropy
metric.
This class provides an implementation of the Height metric.
This 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).
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).
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 efficient implementation of a non-monotonic and
non-uniform entropy metric.
This class provides an implementation of a weighted precision metric as
proposed in:
Sweeney, L. (2002).
Sweeney, L. (2002).
This class provides an implementation of a monotonic weighted precision metric.
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.
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 Game Theoretic Framework for Analyzing Re-Identification Risk.
This class provides an implementation of a static metric in
which information loss is user-defined per generalization level.
This class provides an abstract skeleton for the implementation of weighted metrics.
This class implements a large portion of the model used by the GUI.
The currently selected perspective
Anonymization configuration
Search type
Transformation type
This class implements an entry for the audit trail.
Find and replace entry
This class implements a model for the b-likeness privacy model
This class represents a model
A model for the clipboard.
This class represents an input or output configuration.
A base class for models for criteria.
This class implements a model for the d-disclosure privacy criterion
This class implements a model for the (e,d)-DP criterion.
This class implements a model for the d-presence criterion.
This class implements an event for model changes.
The part of the model that has changed.
This class implements a base class for explicit privacy criteria,
i.e., ones that are associated to a specific attribute
This class implements a (marker) base-class for implicit criteria.
This class implements a model for the k-anonymity criterion.
This class implements a model for the k-map criterion.
This class implements a model for the l-diversity criterion.
A model for local recoding
Possible modes for local recoding
This class implements a filter for a generalization lattice.
This class provides a model for the game-theoretic privacy model
The attacker model used by the privacy model
A model for risk analysis
A enum for statistical models underlying attribute analyses
A enum for views
This class implements a model for risk-based criteria
This class implements a model for the t-closeness criterion.
The transformation mode associated with an attribute
This class models the current view configuration.
Mode.
Implements a classifier
A classification result
Implements a classifier
A classification result
Implements a classifier
A classification result
Implements a classifier
A classification result
The t-closeness criterion for ordered attributes.
Column layout for pageable tables
Adapted from the Nebula source.
Adapted from the Nebula source.
Implements the parameter calculation for differential privacy as proposed in:
Bild R, Kuhn KA, Prasser F.
Class supporting parameter calculations and translations.
This criterion ensures that the population uniqueness falls below a given threshold.
An abstract base class for privacy criteria.
Privacy model for the game theoretic approach proposed in:
A Game Theoretic Framework for Analyzing Re-Identification Risk.
Privacy model for the "no-attack" variant of the game theoretic approach proposed in:
A Game Theoretic Framework for Analyzing Re-Identification Risk.
Privacy model for the game theoretic approach proposed in:
A Game Theoretic Framework for Analyzing Re-Identification Risk.
Privacy model for the "no-attack" variant of the game theoretic approach proposed in:
A Game Theoretic Framework for Analyzing Re-Identification Risk.
Simple progress analysis using moving averages
Aggregate function for multi-dimensional quality measures
Basic configuration for quality models
Parser for ranges
Base class for representing domain shares in this package
Raw domain-share (unencoded).
This class represents a set of domain shares for an attribute.
Base class for quality measures.
Quality measures for individual attributes.
A measure for the complete dataset.
A class encapsulating information about data quality
Implementation of the Loss measure, as proposed in:
Iyengar, V.: Transforming data to satisfy privacy constraints.
Iyengar, V.: Transforming data to satisfy privacy constraints.
Implementation of the Non-Uniform Entropy measure that can handle local recoding.
Implementation of the Precision measure, as proposed in:
L.
L.
Implementation of the mean squared error for individual columns
Implementation of the AECS measure, as proposed in:
K.
K.
Implementation of the Ambiguity measure, as described in:
Goldberger, Tassa: "Efficient Anonymizations with Enhanced Utility" Trans Data Priv
Goldberger, Tassa: "Efficient Anonymizations with Enhanced Utility" Trans Data Priv
Implementation of the Discernibility measure, as proposed in:
R.
R.
Implementation of the Sum of Squared Errors introduced in the supplementary material to:
D.
D.
SSE / SST as described in Solanas, Agusti, Antoni Martinez-Balleste, and J.
The recursive-(c,l)-diversity criterion.
This exception is raised if a privacy or risk model cannot be reliably implemented.
This class provides access to basic resources.
Abstract class for criteria that ensure that a certain risk measure is lower than or equal to a given threshold
A builder for risk estimates
A builder for risk estimates, interruptible
A class for analyzing attribute-related risks.
This class implements a cost/benefit analysis following the game-theoretic approach proposed in:
A Game Theoretic Framework for Analyzing Re-Identification Risk.
A Game Theoretic Framework for Analyzing Re-Identification Risk.
This class encapsulates information about equivalence classes in a data set
Class for risks based on population uniqueness.
The statistical model used for computing Dankar's estimate.
Class representing the distribution of risks in the sample
Class for analyzing re-identification risks of the current sample and mixed
risks which have been derived from privacy models
This class implements risk measures as proposed by El Emam in
"Guide to the De-Identification of Personal Health Information",
"Measuring the Probability of Re-Identification"
Journalist risk
Marketer risk
Prosecutor risk
A set of derived risk estimates
Class for risks based on sample uniqueness
This class implements risk measures as proposed by El Emam in
"Guide to the De-Identification of Personal Health Information",
"Measuring the Probability of Re-Identification" considering
suppressed values as a wildcard
This exception is raised if the method that was called has left output data in an
inconsistent state that may breach privacy.
A set of rows.
An abstract base class for sample-based privacy criteria.
This criterion ensures that the sample uniqueness falls below a given threshold.
A class offering basic descriptive statistics about data handles.
A class offering basic descriptive statistics about data handles.
Statistics representing the performance of various classifiers
A ROC curve
A contingency table.
An entry in the contingency table.
Statistics about the equivalence classes.
A frequency distribution.
Encapsulates statistics obtained using various quality models
A base class for summary statistics
Style config for fill layouts.
Header style.
Sets up rendering style used for selected areas and the selection anchor.
The table style configuration.
This class provides some utility methods for working with SWT.
An abstract base class for t-closeness criteria as proposed in:
Li N, Li T, Venkatasubramanian S.
A class that supports associating input with output
For hash tables
Internal class for unexpected errors.
This class checks the version number
This view lists all attributes and their metadata
This view displays basic attribute information.
This class allows to define weights for attributes.
This class displays the clipboard.
This class allows to configure the coding model.
This view displays the montary amounts used for cost/benefit analyses
A view on a
Data object.A view on a
Data object.A view on a
Data object.This class displays a filter for the lattice.
This class implements a view of a lattice.
This class implements a list view on selected nodes.
This view displays the population settings
This class displays a list of all defined privacy criteria.
This view displays properties about the currently selected transformation.
This view displays basic properties about input or output data.
This view displays basic properties about input data.
This view displays basic properties about output data.
This is a base class for displaying risk estimates.
This view displays risk estimates according to different attacker models
This view displays the identified Safe Harbor identifier.
This view displays basic risk estimates.
This view displays basic risk estimates.
This view allows to select a subset of the quasi-identifiers
This view displays basic risk estimates.
This view displays basic risk estimates.
This class provides an abstract base for views that display parts of the solution space
This is a base class for displaying utility data.
This is a base interface for simple views in this category
This view displays a statistics about the performance of logistic regression classifiers
This view allows to select a set of attributes for classification analysis
This view shows a hint message regarding attribute selection for classification analysis
This view displays a statistics about the performance of logistic regression classifiers
This view displays a statistics about the performance of logistic regression classifiers
This class displays a contingency table as a heat map.
This class displays a contingency table.
This view displays a frequency distribution.
This view displays a frequency distribution.
This view displays statistics about the distribution of equivalence classes
This view displays results from different quality models
This view displays summary statistics.
This view displays basic information about the research subset.
This class implements a tiles view on selected nodes.
This view displays general settings regarding data transformation.
This view displays settings regarding the utility metrics.
An abstract base class for the XML vocabulary.
First version of the ARX XML vocabulary.
Second version of the ARX XML vocabulary.
Class for accessing the water mark
A base class for workers that perform asynchronous tasks in a progress dialog.
This worker performs the anonymization process.
This worker creates a certificate.
This worker exports data to disk.
This worker loads external data.
This worker loads a project file from disk.
This worker that optimizes a transformation.
This worker saves a project file to disk.
This worker applies a transformation.
Helper class
Helper class
The default XML handler.
A writer for XML documents that can handle common objects from the ARX model.
This internal class provides access to version 2 of all metrics.