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Redfield_​Privacy_​extension_​example

The workflow gives an example of how the Privacy extension nodes are used for data anonymization and risk assessment.

A simple example is shown how to anonymize «personal» data taken from computer game FIFA19. Multiple anonymization techniques are used: hashing, generalization, masking etc. In the end of the workflow there goes risk assessment and comparison of original and anonymized data sets. For risk analysis the distinction and separation of quasi-identifiers and different attacker models are estimated.

Reading and pre-processing Risk assessment Anonymization ValueRead and updateAge hierarchyClubRisks:originalvsanonymized Save the hierarchiesHash nameRead FIFA data setTake the first 50 clubswith the highest averageRelease ClauseNode 473 Create Hierarchy Hierarchy Reader Create Hierarchy AnonymityAssessment Hierarchy Writer Visualization ofthe QI attributes Anonymization Pre-processing CSV Reader Club selector HierarchicalAnonymization Reading and pre-processing Risk assessment Anonymization ValueRead and updateAge hierarchyClubRisks:originalvsanonymized Save the hierarchiesHash nameRead FIFA data setTake the first 50 clubswith the highest averageRelease ClauseNode 473 Create Hierarchy Hierarchy Reader Create Hierarchy AnonymityAssessment Hierarchy Writer Visualization ofthe QI attributes Anonymization Pre-processing CSV Reader Club selector HierarchicalAnonymization

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: artem
Created at: 2019-10-21
On NodePit since: 2019-11-15
Last update: 2024-12-26
Created with KNIME version: v4.0.2
Tags: PrivacyAnonymizationRedfieldGDPR

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