![]() start-at STEP Choose a step to begin the process (inclusive). Locale setting to initialize fake data generation. This will be dropped at the end of theĭatabase credentials: username. Provided, a unique name will be generated from the Name of database to restore and anonymize in. More databases willīe supported in future versions. The destination filepath to write the dumped output Use `-` forĪ strategyfile to use during anonymization. h, -help show this help message and exit ] Ī tool for writing better anonymization strategies for your production The anonymize process can be performed on remote servers, but you are responsible for creating/managing the target database.This is because MSSQL RESTORE and BACKUP instructionsĪre received by the database, so piping a local backup to a remote server is not possible. For RESTORE_DB/ DUMP_DB operations, the database server must be running.Requires extra dependencies: install package pynonymizer.If this workflow doesnt work for you, see process control to see if it can be adjusted to suit your needs. Anonymize temporary database with strategy.Restore from dumpfile to temporary database.You can see strategyfile examples for existing database, such as wordpress or adventureworks sample database, in the the examples folder. 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. Ē1.14.5.0 Ē0221118T163152.Pynonymizer is a universal tool for translating sensitive production database dumps into anonymized copies. extracted field still has unmasked data.īelow is the type of event we expect: Product Assembly Name Product VersionĜlass Name Timestamp Severity Hostname User name User ID WebEngine Request IDĜonnection ID Task IDĞxecution ID Report ID Request ID Transformation ID MessageĞxception Stacktrace Thank you for your response tried what you suggested but if did not work. "User name" could be combination of id and name and we only want to mask name: The field User name is in the middle and follows hostname and hence GBW is this example. Related info: We are expecting tab-delimited data. My SED is on universal forwarder (windows) and it works fine for raw data: While below works to mask data in raw, it does not work for extracted field "User name". We have requirement to mask data in index time.
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