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.
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.
The “New Multi Table Protection Job …” wizard in IRI Workbench described in this article is one of the ways that IRI FieldShield product (or IRI Voracity platform) users can automatically mask personally identifiable information (PII) in database columns that are part of a foreign key relationship, and thus preserve referential integrity between the tables.
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.
This article focuses on IRI Workbench execution options for scripts based on the SortCL program language, which covers IRI Voracity ETL, CDC, SDC, pivoting and subsetting jobs, as well as its constituent product jobs; i.e.,
Users of PII masking tools like FieldShield, DarkShield, and CellShield EE in the IRI Data Protector Suite or Voracity platform can catalog and search their data — and apply data transformation and protection functions as rules — using built-in data classification infrastructure in their common front-end IDE, IRI Workbench, built on Eclipse™.
IRI provides multiple data discovery features for personally identifiable information (PII) and other sensitive or need-to-be-found data held in enterprise sources.
Beyond data class, pattern- and fuzzy-match searches described elsewhere in this blog, this article primarily discusses the search for values held in a lookup or ‘set’ file (e.g.,