Package | Description |
---|---|
org.deidentifier.arx.criteria |
This package implements different variants of class-based privacy criteria,
such as k-anonymity, l-diversity, t-closeness and d-presence.
|
org.deidentifier.arx.framework.check.groupify |
This package implements different hash-groupify operators.
|
org.deidentifier.arx.framework.check.transformer |
This package provides several transformers that efficiently implement the 'projection' optimization.
|
org.deidentifier.arx.metric |
Package providing access to quality models
|
org.deidentifier.arx.metric.v2 |
Main package implementing quality models
|
Modifier and Type | Method and Description |
---|---|
boolean |
SampleBasedCriterion.isAnonymous(Transformation<?> node,
HashGroupifyEntry entry)
Not supported by this type of criterion
|
boolean |
RecursiveCLDiversity.isAnonymous(Transformation<?> node,
HashGroupifyEntry entry) |
boolean |
ProfitabilityProsecutorNoAttack.isAnonymous(Transformation<?> node,
HashGroupifyEntry entry) |
boolean |
ProfitabilityProsecutor.isAnonymous(Transformation<?> transformation,
HashGroupifyEntry entry) |
boolean |
ProfitabilityJournalistNoAttack.isAnonymous(Transformation<?> node,
HashGroupifyEntry entry) |
abstract boolean |
PrivacyCriterion.isAnonymous(Transformation<?> node,
HashGroupifyEntry entry)
Implement this, to enforce the criterion.
|
boolean |
OrderedDistanceTCloseness.isAnonymous(Transformation<?> node,
HashGroupifyEntry entry) |
boolean |
KMap.isAnonymous(Transformation<?> node,
HashGroupifyEntry entry) |
boolean |
KAnonymity.isAnonymous(Transformation<?> node,
HashGroupifyEntry entry) |
boolean |
Inclusion.isAnonymous(Transformation<?> node,
HashGroupifyEntry entry) |
boolean |
HierarchicalDistanceTCloseness.isAnonymous(Transformation<?> node,
HashGroupifyEntry entry) |
boolean |
EqualDistanceTCloseness.isAnonymous(Transformation<?> node,
HashGroupifyEntry entry) |
boolean |
EntropyLDiversity.isAnonymous(Transformation<?> node,
HashGroupifyEntry entry) |
boolean |
EnhancedBLikeness.isAnonymous(Transformation<?> node,
HashGroupifyEntry entry) |
boolean |
EDDifferentialPrivacy.isAnonymous(Transformation<?> node,
HashGroupifyEntry entry) |
boolean |
DPresence.isAnonymous(Transformation<?> node,
HashGroupifyEntry entry) |
boolean |
DistinctLDiversity.isAnonymous(Transformation<?> node,
HashGroupifyEntry entry) |
boolean |
DDisclosurePrivacy.isAnonymous(Transformation<?> node,
HashGroupifyEntry entry) |
boolean |
BasicBLikeness.isAnonymous(Transformation<?> node,
HashGroupifyEntry entry) |
Modifier and Type | Field and Description |
---|---|
HashGroupifyEntry |
HashGroupifyEntry.next
The next element in this bucket.
|
HashGroupifyEntry |
HashGroupifyEntry.nextOrdered
The overall next element in original order.
|
Modifier and Type | Method and Description |
---|---|
HashGroupifyEntry |
HashGroupify.getEntry(int[] tuple)
Returns the entry for the given tuple
|
HashGroupifyEntry |
HashGroupify.getFirstEquivalenceClass()
Returns the first entry
|
Modifier and Type | Method and Description |
---|---|
void |
MetaHashGroupify.add(HashGroupifyEntry _entry)
Adds a tuple from the buffer
|
boolean |
HashGroupify.isCompletelyGeneralized(HashGroupifyEntry entry)
Returns whether the given entry is completely generalized
|
Modifier and Type | Method and Description |
---|---|
void |
AbstractTransformer.init(long projection,
int[] state,
HashGroupify groupify,
HashGroupify source,
int[] snapshot,
TransformationCheckerStateMachine.TransitionType transition,
int startIndex,
int stopIndex,
HashGroupifyEntry element,
DataMatrix buffer)
Prepares the next transformation
|
Modifier and Type | Method and Description |
---|---|
InformationLossWithBound<T> |
Metric.getInformationLoss(Transformation<?> node,
HashGroupifyEntry entry)
Returns the information loss that would be induced by suppressing the given entry.
|
Modifier and Type | Method and Description |
---|---|
static double |
MetricSDNMEntropyBasedInformationLoss.getEntropyBasedInformationLoss(Transformation<?> transformation,
HashGroupifyEntry entry,
DomainShare[] shares,
DataAggregationInformation aggregation,
double maxIL)
Implements the entropy-based IL model.
|