Quasi-identifiers, or indirect identifiers, are personal attributes that are true about, but not necessarily unique, to an individual. Examples are one’s age or date of birth, race, salary, educational attainment, occupation, marital status and zip code.
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Adding ‘random noise’ to data through blurring or perturbation is a data common anonymization requirement for researchers and marketers of protected health information (PHI) seeking to comply with the HIPAA Expert Determination Method security rule.
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According to Simson L. Garfinkel at the NIST Information Access Division’s Information Technology Laboratory,
De-identification is not a single technique, but a collection of approaches, algorithms, and tools that can be applied to different kinds of data with differing levels of effectiveness.
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The IRI Workbench GUI, built on Eclipse™, provides several ways to create data-driven jobs … jobs which are ultimately expressed in self-documenting plain-text scripts and metadata files.
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There are times when it is necessary to test with or share data that has elements of personally identifiable information (PII). To comply with data privacy laws and prevent a data breach, you may need to provide data that reflects, and sometimes imparts, critical information, but still protects the PII.
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In the last couple of years since this article about the use of IRI CoSort as a parallel data manipulation alternative to Hadoop, IRI released the Voracity platform to process big data in either paradigm.
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Many of the same data manipulation, masking, and test data generation jobs you can run in IRI Voracity® with the default SortCL program can now also run seamlessly in Hadoop™.
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Just as production data processing tools like IRI CoSort must handle big data in NoSQL DB environments, so too must a big test data generation tool like IRI RowGen.
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Unlike the New DB Test Data Job wizard for creating multiple, related test tables, the New Test Data Job wizard in the IRI Workbench GUI for RowGen generates individual test sets.
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IRI RowGen software creates test data you can customize to meet specific needs. It supports the formats and techniques that make your test sets as realistic as you want them to be.
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