![]() Note that when you select a random date, this date can be in the future or in the past. This is not user-friendly, therefore I would not recommend using this for acceptance test data. Random valueWhen you use this option to anonymize your data, a random value is generated that meets the domain specifications. For example, if you have a sample data set with random dates and numbers, only valid dates will be used to anonymize a “birthday” column. Upon anonymization, a random value from the set will be picked, taking into account the domain of the column. Sample data setThanks to the guys at Software Modernization, default sample data sets are available that you can use to anonymize your data. They can be recognized by the icon below and are hidden by the undecided columns prefilter. You can choose between:įoreign key columns are automatically anonymized based on the settings of the source column. A prefilter is provided to suggest which columns are sensitive, based on keywords or because columns with the same name or domain are also marked sensitive.įor sensitive columns, it is mandatory to select an anonymization type. View-columns, calculated fields, and identity fields are not data columns and therefore are not shown. With this new feature, we enable you to set the sensitivity for each data column of your application in the “Data sensitivity” modeler. This feature can not only be used for personal data, but also for other sensitive data like passwords or credit card numbers. ![]() To help our customers comply with this new legislation, we added a new feature “Data sensitivity” to the Thinkwise Platform to automate the anonymization of personal data in any Thinkwise application. With the introduction of Europe’s General Data Protection Regulation, it is more important than ever for companies that their IT environment is secure and meets the legal requirements for privacy protection.
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