g9 Anonymizer
The introduction of GDPR has major implications for the processing of personal and sensitive data. The g9 Anonymizer tools make it easy to create JDBC applications that provide compliance to various GDPR requirements and g9 Anonymizer can produce databases anonymized for test- and development.
g9 Anonymizer helps you
How does it work?
g9 Anonymizer: Changes data through masking and anonymization
- Make data unrecognizable: you can mask, shuffle and randomize data using inputs from many sources
- Enables you to anonymize person identifiable data like names and social security numbers
- Define masking and anonymization rules using sequences, random selections, data from files and database columns
- Decide which data to mask using where clauses and parameters selection
- Usable data for developers in test/development without possibility to identify sensitive information
- Extend with your own algorithms for type conversion and value transformation securing correct value or syntax
- Make your own rules and add them into different tasks, each one run separately or combined
g9 Anonymizer: Creates a subset of your database
- Reduce database size for test purposes. A production database is often big and it takes time to create copies. A reduced size database makes every day life better for developers
- Removal of data instances supports referential integrity in any depth. One simple definition may remove data from many tables dependent on each other
- You may add dependencies if foreign key definitions are missing
- Scale down and keep representative data for test
g9 Anonymizer: Creates synthetic data to deal with special test cases
- Get quickly started from empty database, adding data content defined by mask rules
- Create records supporting your special test cases
- Data creation supports referential integrity in any depth
- Adding children with flexible distribution of foreign keys across all tables
- Extend with your own defined algorithm for distribution of child records
g9 Anonymizer: Same masking across many tables and databases ensures consistency
- Supports referential integrity over several tables in any depth
- Manual added dependencies will act as good as foreign keys
- Add integrity for logical pointers across all relevant databases using mapping and encryption
- The generated program may be used towards different RDBMSs
g9 Anonymizer: Protects developers from person identifiable data
- Developers/testers will often be exposed for data they shouldn’t see
- Use g9 Anonymizer to anonymize, mask, subset and create data for test and development databases
- g9 Anonymizer improves and speeds up development of data masking and anonymization
- GDPR changes the development processes
g9 Anonymizer: Imports database schema from your database
- The Anonymizer Project wizard guides you through all tasks
- Add a proper database driver and setup connection to the database
- Fetch the database schema and you are ready to define rules
g9 Anonymizer: Rules expressed in an intuitive and easy to use editor
- Database pane showing necessary schema information
- Task pane to create data masking and anonymization rules
- Property View to set and view all rule details
- Problems View giving errors and warnings feedback on all rules
- Project Explorer to view generated java source
g9 Anonymizer: Generates the masking program
- Generates a program from your masking and anonymization rules
- The program does it all, your obligation is only to run it
- No lock-in, you can maintain the generated code separately
- Supports JDBC based RDBMS like Oracle, SQL Server, Sybase, MySQL, PostgreSQL
- May be run as CLI program or be embedded in your application of choice
- Simple, repeatable, and easy to use. You may run the generated program many times
