# Package org.deidentifier.arx.examples

Class Summary
Example: This class provides a base class for examples.
Example1: This class implements an example on how to use the API by directly providing the input datasets.
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.
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.
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.
Example13: This class implements an simple example for using multiple sensitive attributes and different privacy models.
Example14: This class implements an example on how to use the Loss Metric.
Example16: This class implements an example of how to use the API for access to basic statistics about the data.
Example17: This class implements an example of how to list the available data types.
Example18: This class implements examples of how to use the builders for generalization hierarchies.
Example19: This class implements an example of how to use the API for creating different output representations of an input dataset.
Example2: This class implements an example on how to use the API by providing CSV files as input.
Example20: This class implements examples of how to use aggregate functions.
Example21: This class demonstrates the use of the data import facilities provided by the ARX framework.
Example22: This class implements an example on how to use the l-diversity privacy model without protecting sensitive assocations.
Example23: This class implements an example on how to use multiple instances of l-diversity without protecting sensitive associations.
Example24: This class implements examples on how to directly use empty and functional hierarchies.
Example25: This class implements an example for using the generalized loss metric with different types of generalization hierarchies.
Example26: This class implements examples of how to use an interval-based hierarchy builder with high precision .
Example27: This class implements an example on how to use data cleansing capabilities
Example28: This class implements an example on how to use data cleansing using the DataSource functionality.
Example29: This class implements an example of how to perform risk analyses with the API
Example3: This class implements an example on how to use the API by directly providing the input datasets.
Example30: This class implements an example of how to compute summary statistics
Example31: This class implements an example of how to use microaggregation
Example32: This class implements an example of how to use microaggregation with generalization
Example33: This class implements an example of how to use microaggregation.
Example34: This class implements an example of how to use the heuristic search algorithm
Example35: This class implements an example of how to find HIPAA identifiers.
Example36: This class implements an example of how to use utility-based microaggregation
Example37: This class implements an example of how to use (e,d)-DP
Example38: This class implements an example of how to use local recoding with ARX
Example39: This class implements an example on how to compare data mining performance
Example4: This class implements an example on how to use the API for tools such as GUIs.
Example40: This class implements an example on how to compare data mining performance
Example41: This class implements an example on how to use the k-map model
Example42: This class implements an example on how to use the k-map and d-presence models combined
Example43: This class implements an example of how to evaluate combined risk metrics
Example44: This class implements an example of how to use the k-map privacy model with a statistical estimator.
Example45: This class implements an example of how to use the mixed risk model.
Example46: This class implements an example of how to use the distribution of risks
Example47: Example for evaluating distinction and separation of attributes as described in R.
Example48: This class implements an example of how to use ordered distance t-closeness.
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.
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
Example54: This class implements an example on how to access quality statistics
Example55: This class implements an example of how to use a fast algorithm for local recoding with ARX
Example56: This class implements an example of how to evaluate risk with wildcard matching
Example57: This class implements an example on how to analyze risks with wildcards for data transformed with cell suppression
Example58: This class implements an example that shows consistent handling of suppressed records in input and output
Example59: This class implements an example that shows handling of suppressed values and records in input data
Example6: This class implements an example on how to use the API for tools such as GUIs.
Example60: This class implements an example of processing highdimensional data
Example7: This class implements an example on how to use the API for tools such as GUIs.
Example8: This class implements an example on how to apply the t-closeness criterion.
Example9: This class implements an example on how to apply the d-presence criterion.