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02_​Anonymize_​personal_​data

Anonymize Personal Data

This example shows one way of anonymizing data. it uses the approved adults data set. For this example, distance matrix are calculated for all relevant rows then k-nearest Neighbors is used to find the "closest" by default 2 records to the original. A record to replace the original is then built by randomly choosing values from the closest neighbors. To test the anonymized data, a standard machine learning excersize is performed on the anonymized data, the original data and also by applying the anonymized model to the original data. Measures of quality are captured. Other methods of testing quality could be used. To test whether the data is truly anonymized a test is performed to attempt to trace back from the equivalent anonymized record to the original record. Other approaches for deanonymizing could be used. For further details, please refer to the white paper "Taking a proactive approach to GDPR with KNIME"

Choose your own methods totest the quality of theanonymisation Choose your own methods to anonymize Data Choose your own methods totest the identification of anoriginal record 2. Anonymize Personal DataThis example shows one way of anonymizing data. it uses the approved adults data set. For this example, distance matrix are calculated for all relevant rows then k-nearest Neighbors is used to find the"closest" by default 2 records to the original. A record to replace the original is then built by randomly choosing values from the closest neighbors.To test the anonymized data, a standard machine learning excersize is performed on the anonymized data, the original data and also by applying the anonymized model to the original data. Measures of qualityare captured. Other methods of testing quality could be used. To test whether the data is truly anonymized a test is performed to attempt to trace back from the equivalent anonymized record to the original record. Other approaches for deanonymizing could be used. For further details, please refer to the white paper "Taking a proactive approach to GDPR with KNIME" Approved dataremove rowswith missingvaluessavequality informationsaveDeanonymizationinformationanonymizeddata Table Reader Missing Value Create DistanceMatrix Find k-nearest Neighborsbased on distance matrix Table Writer Random Value Selectionfrom nearest neighbors Quality Test Deanonymizationcheck Table Writer Table Writer Default onSUBSET of Data Choose your own methods totest the quality of theanonymisation Choose your own methods to anonymize Data Choose your own methods totest the identification of anoriginal record 2. Anonymize Personal DataThis example shows one way of anonymizing data. it uses the approved adults data set. For this example, distance matrix are calculated for all relevant rows then k-nearest Neighbors is used to find the"closest" by default 2 records to the original. A record to replace the original is then built by randomly choosing values from the closest neighbors.To test the anonymized data, a standard machine learning excersize is performed on the anonymized data, the original data and also by applying the anonymized model to the original data. Measures of qualityare captured. Other methods of testing quality could be used. To test whether the data is truly anonymized a test is performed to attempt to trace back from the equivalent anonymized record to the original record. Other approaches for deanonymizing could be used. For further details, please refer to the white paper "Taking a proactive approach to GDPR with KNIME" Approved dataremove rowswith missingvaluessavequality informationsaveDeanonymizationinformationanonymizeddataTable Reader Missing Value Create DistanceMatrix Find k-nearest Neighborsbased on distance matrix Table Writer Random Value Selectionfrom nearest neighbors Quality Test Deanonymizationcheck Table Writer Table Writer Default onSUBSET of Data

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Created by: koetter
Created at: 2017-06-29
On NodePit since: 2024-05-05
Last update: 2024-12-17
Created with KNIME version: v4.0.2
Tags: GDPRCustomer IntelligenceLegalLawPrivacyData Lineage

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