What is Privacy Analytics Data Masking Tool?
Included with PARAT is our Intelligent Masking Capability for testing health IT applications. PARAT generates realistic test data for production systems thus greatly reducing breach risks as compared to using real data. The solution is to mask and de-identify production data for use in functional and performance testing.The PARAT tool is the only one on the market today that combines masking with powerful de-identification capabilities. This means that we can: (a) ensure maximum data quality/utility for the output data (for example, retaining relationships among dates), and (b) quantitatively demonstrate that the risk of re-identification is "very small" as defined in the HIPAA Privacy Rule.Testing teams need data that recreates the situations that appear in practice - in production. PARAT is able to retain many of the original data characteristics by being selective in the transformations performed on the data, and by using powerful optimization algorithms to balance data utility with re-identification risk.
Benefits and Insights
Why use Privacy Analytics Data Masking Tool?
Key differentiators & advantages of Privacy Analytics Data Masking Tool
- Built specifically for clinical and health claims databases
- Produces realistic test data (i.e., retains the characteristics of the original data) ensuring unusual patterns in the data will also appear during testing
- Works on massive databases in a continuous mode for rapid refresh cycles of test data
- Saves masking function specifications that can be executed again on other databases and at scheduled intervals
- Includes library of templates and reference databases for most common direct identifiers often seen in clinical and claims data sets
- Maintains referential integrity across multiple tables to ensure patient information is masked consistently
- Methods ensure that an adversary is unable to reverse engineer masks.
- Ability to rapidly processes hundreds of gigabytes of data spread across multiple tables
- Masks keys in tables to retain the same field type and size, maintaining referential integrity in tables