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01_​Identify_​PII_​and_​Special_​Category_​Data

Workflow

Identify PII and Special Category Data
GDPRCustomer IntelligenceLegalLawPrivacyData Lineage
Use your favorite technique todetermine how closely related otherfields are at predicting identifieddiscriminatory fields.Here a simple linear correlation isused for demonstration purposes 1. Identify PII and Special Category DataThis example is to show techniques for identifying personal data and special category data. It uses Quickforms and JavaScript views to surface the informaiton and to capture the decisions made.If special category data is identified then a simple linear correlation is run to see if there is any strong correlation to any of the other data - if so, this might be an indication that we should also look at that data.A decision can be made and will be documented as to whether special category fields as well as any other fields correlated to that field should be kept or deleted.Information about the workflow and its context information (who wrote it, when, etc.) as well as detailed statistics about the data and the decisions made about that data are captured.The interactive wrapped metanodes (identify PII and Special Category Fiels, Identify Pseudo Special Category Fields and Decide in Inclusion) can be run either on the KNIME Analytics Platform (ie on the desktop) or can beused with the KNIME Server and the KNIME Web Portal.For more details, please refer to the white paper "Taking a proactive approach to GDPR with KNIME" Interactive wrapped metanode Interactive wrapped metanode Interactive wrapped metanode(enhanced)Adults.tableSave DecisionCriteria and statssavecontext informationChecked/ApprovedDataSave tablewith approveddata(used by othe GDPR examples) Table Reader Table Writer Decide on Inclusion Table Writer ReferenceColumn Filter Table Writer Identify PII and SpecialCategory Fields Pull Informationabout the workflow Pull informationabout columns Joiner identify Pseudo SpecialCategory Fields Use your favorite technique todetermine how closely related otherfields are at predicting identifieddiscriminatory fields.Here a simple linear correlation isused for demonstration purposes 1. Identify PII and Special Category DataThis example is to show techniques for identifying personal data and special category data. It uses Quickforms and JavaScript views to surface the informaiton and to capture the decisions made.If special category data is identified then a simple linear correlation is run to see if there is any strong correlation to any of the other data - if so, this might be an indication that we should also look at that data.A decision can be made and will be documented as to whether special category fields as well as any other fields correlated to that field should be kept or deleted.Information about the workflow and its context information (who wrote it, when, etc.) as well as detailed statistics about the data and the decisions made about that data are captured.The interactive wrapped metanodes (identify PII and Special Category Fiels, Identify Pseudo Special Category Fields and Decide in Inclusion) can be run either on the KNIME Analytics Platform (ie on the desktop) or can beused with the KNIME Server and the KNIME Web Portal.For more details, please refer to the white paper "Taking a proactive approach to GDPR with KNIME" Interactive wrapped metanode Interactive wrapped metanode Interactive wrapped metanode(enhanced)Adults.tableSave DecisionCriteria and statssavecontext informationChecked/ApprovedDataSave tablewith approveddata(used by othe GDPR examples)Table Reader Table Writer Decide on Inclusion Table Writer ReferenceColumn Filter Table Writer Identify PII and SpecialCategory Fields Pull Informationabout the workflow Pull informationabout columns Joiner identify Pseudo SpecialCategory Fields

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Nodes

01_​Identify_​PII_​and_​Special_​Category_​Data consists of the following 74 nodes(s):

Plugins

01_​Identify_​PII_​and_​Special_​Category_​Data contains nodes provided by the following 5 plugin(s):