DOWNLOAD ANONYMIZER UNIVERSAL SETUP FULL
For a full list of data generation strategies, see the docs on strategyfiles Examples This may or may not suit your exact use-case. This process is chosen for compatibility and speed of operation, but does not guarantee uniqueness. Pynonymizer's main data replacement mechanism fake_update is a random selection from a small pool of data ( -seed-rows controls the available Faker data). There are a wide variety of data types available which should suit the column in question, for example: Pynonymizer replaces personally identifiable data in your database with realistic pseudorandom data, from the Faker library or from other functions. It canīe used to run better staging environments, integration tests, and even simulate database migrations.īelow is an excerpt from an anonymized database: does it work? With Anonymized databases, copies can be processed regularly, and distributed easily, leaving your developers and testers with a rich source of information on the volume and general makeup of the system in production. Anonymized databases allow us to use the structures present in production, while stripping them of any personally identifiable data that wouldĬonsitute a breach of privacy for end-users and subsequently a breach of GDPR. Than one that is artificially created by developers or by testing frameworks. In most situations, the production dataset is usually significantly larger than any development copy, andįrom time to time, it is prudent to run a new feature or stage a test against this dataset, rather
The primary source of information on how your database is used is in your production database.
This can help you support GDPR/Data Protection in your organization without compromizing on quality testing data. Pynonymizer is a universal tool for translating sensitive production database dumps into anonymized copies.
0 kommentar(er)
