Class | Description |
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Example |
This class provides a base class for examples.
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Example1 |
This class implements an example on how to use the API by directly providing
the input datasets.
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Example10 |
This class implements an example on how to apply the d-presence privacy model
and create a research subset by providing a data subset.
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Example11 |
This class implements an example on how to apply the d-presence privacy model
and create a research subset by providing a data selector.
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Example12 |
This class implements an example on how to apply the d-presence privacy model
and create a research subset by providing a complex data selector.
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Example13 |
This class implements an simple example for using multiple sensitive attributes and
different privacy models.
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Example14 |
This class implements an example on how to use the Loss Metric.
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Example16 |
This class implements an example of how to use the API for access to basic statistics
about the data.
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Example17 |
This class implements an example of how to list the available data types.
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Example18 |
This class implements examples of how to use the builders for generalization hierarchies.
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Example19 |
This class implements an example of how to use the API for creating different
output representations of an input dataset.
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Example2 |
This class implements an example on how to use the API by providing CSV files
as input.
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Example20 |
This class implements examples of how to use aggregate functions.
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Example21 |
This class demonstrates the use of the data import facilities provided by the
ARX framework.
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Example22 |
This class implements an example on how to use the l-diversity privacy model
without protecting sensitive assocations.
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Example23 |
This class implements an example on how to use multiple instances of l-diversity
without protecting sensitive associations.
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Example24 |
This class implements examples on how to directly use empty and functional
hierarchies.
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Example25 |
This class implements an example for using the generalized loss metric with different types of
generalization hierarchies.
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Example26 |
This class implements examples of how to use an interval-based hierarchy builder
with high precision .
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Example27 |
This class implements an example on how to use data cleansing capabilities
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Example28 |
This class implements an example on how to use data cleansing using the DataSource functionality.
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Example29 |
This class implements an example of how to perform risk analyses with the API
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Example3 |
This class implements an example on how to use the API by directly providing
the input datasets.
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Example30 |
This class implements an example of how to compute summary statistics
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Example31 |
This class implements an example of how to use microaggregation
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Example32 |
This class implements an example of how to use microaggregation with generalization
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Example33 |
This class implements an example of how to use microaggregation.
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Example34 |
This class implements an example of how to use the heuristic search algorithm
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Example35 |
This class implements an example of how to find HIPAA identifiers.
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Example36 |
This class implements an example of how to use utility-based microaggregation
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Example37 |
This class implements an example of how to use (e,d)-DP
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Example38 |
This class implements an example of how to use local recoding with ARX
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Example39 |
This class implements an example on how to compare data mining performance
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Example4 |
This class implements an example on how to use the API for tools such as GUIs.
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Example40 |
This class implements an example on how to compare data mining performance
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Example41 |
This class implements an example on how to use the k-map model
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Example42 |
This class implements an example on how to use the k-map and d-presence models combined
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Example43 |
This class implements an example of how to evaluate combined risk metrics
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Example44 |
This class implements an example of how to use the k-map privacy model with a statistical estimator.
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Example45 |
This class implements an example of how to use the mixed risk model.
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Example46 |
This class implements an example of how to use the distribution of risks
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Example47 |
Example for evaluating distinction and separation of attributes as described in
R.
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Example48 |
This class implements an example of how to use ordered distance t-closeness.
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Example49 |
Examples of using the no-attack variant of the game-theoretic approach for
performing a monetary cost/benefit analysis using prosecutor risk.
|
Example5 |
This class implements an example on how to use the API by directly providing
the input datasets.
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Example50 |
Examples of using the no-attack variant of the game-theoretic approach for
performing a monetary cost/benefit analysis using journalist risk.
|
Example51 |
Examples of using the game-theoretic approach for
performing a monetary cost/benefit analysis using prosecutor risk.
|
Example52 |
Examples of using the game-theoretic approach for
performing a monetary cost/benefit analysis using journalist risk.
|
Example53 |
This class implements an example on how to generate reports
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Example54 |
This class implements an example on how to access quality statistics
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Example55 |
This class implements an example of how to use a fast algorithm for local recoding with ARX
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Example56 |
This class implements an example of how to evaluate risk with wildcard matching
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Example57 |
This class implements an example on how to analyze risks with wildcards for data transformed with cell suppression
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Example58 |
This class implements an example that shows consistent handling of suppressed records in input and output
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Example59 |
This class implements an example that shows handling of suppressed values and records in input data
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Example6 |
This class implements an example on how to use the API for tools such as GUIs.
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Example60 |
This class implements an example of processing highdimensional data
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Example7 |
This class implements an example on how to use the API for tools such as GUIs.
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Example8 |
This class implements an example on how to apply the t-closeness criterion.
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Example9 |
This class implements an example on how to apply the d-presence criterion.
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