Do you need to remove the individualizing characteristics in your files
so that a person or item stored in the original field cannot be identified?
Once anonymized, the data
cannot be linked to any source. The benefit of anonymization over filtering and encrypting is that
the original field layout (position, size, and data type) can remain the same, and look realistic
in test data environments.
Solutions:
CoSort obfuscates and masks files at the field level through its Sort
Control Language (SortCL) data transformation and reporting program. In
fact, SortCL users can apply several techniques to anonymize sensitive data
in flat files, including:
• using expressions and functions on numeric fields
• byte-shifting, data manipulation, or conversion
• masking real characters with alternate
characters
• transforming data with your
own field functions
The method you choose will determine the appearance of the anoymized fields
and the likelihood of recovering the field values. And remember, these techniques
are among several other choices SortCL provides for protecting data at risk,
including encryption, de-identification, and pseudonymization.
In addition to masking real data, there is also a standalone solution for creating realistic test data. IRI's RowGen (test data) package uses the same metadata as CoSort's SortCL program to randomly generate or select data. This product is especially useful if you need to provide anonymous data in product file formats but do have have access to real data.
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