Package org.deidentifier.arx.algorithm

Klasse DataDependentEDDPAlgorithm

java.lang.Object

org.deidentifier.arx.algorithm.AbstractAlgorithm

org.deidentifier.arx.algorithm.DataDependentEDDPAlgorithm

public class DataDependentEDDPAlgorithm
extends AbstractAlgorithm

This class implements the search algorithm used with data-dependent differential privacy as proposed in: Bild R, Kuhn KA, Prasser F. SafePub: A Truthful Data Anonymization Algorithm With Strong Privacy Guarantees. Proceedings on Privacy Enhancing Technologies. 2018(1):67-87.

Feldübersicht

Von Klasse geerbte Felder org.deidentifier.arx.algorithm.AbstractAlgorithm

checker, solutionSpace

Methodenübersicht

Modifizierer und Typ	Methode	Beschreibung
static AbstractAlgorithm	create(SolutionSpace<?> solutionSpace, TransformationChecker checker, boolean deterministic, int expansionLimit, double epsilonSearch, int timeLimit, int stepLimit)	Creates a new instance
boolean	traverse()	Implement this method in order to provide a new algorithm.

Von Klasse geerbte Methoden org.deidentifier.arx.algorithm.AbstractAlgorithm

computeUtilityForMonotonicMetrics, getCheckCount, getCheckLimit, getGlobalOptimum, getTimeLimit, getTimeStart, mustStop, progress, setListener, startTraverse, trackOptimum, trackProgressFromLimits, trackProgressFromLimits

Von Klasse geerbte Methoden java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methodendetails

create

public static AbstractAlgorithm create(SolutionSpace<?> solutionSpace,
 TransformationChecker checker,
 boolean deterministic,
 int expansionLimit,
 double epsilonSearch,
 int timeLimit,
 int stepLimit)

Creates a new instance

Parameter:

solutionSpace -

checker -

deterministic -

expansionLimit -

epsilonSearch -

timeLimit -

stepLimit -

Gibt zurück:

traverse

public boolean traverse()

Beschreibung aus Klasse kopiert: AbstractAlgorithm

Implement this method in order to provide a new algorithm.

Angegeben von:

traverse in Klasse AbstractAlgorithm

Gibt zurück:

Whether the result is optimal