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The A-Z of Privacy
A Privitar Rule defines a specific privacy-enhancing transformation on a column of values in a dataset in order to de-identify it, whilst preserving data utility.
Privitar offers a rich set of Rule types that provide access to a wide range of Privacy Enhancing Techniques, including Redaction, Tokenization, Substitution, Perturbation, Encryption, Generalization, and more.
Rules can be configured in fine-grained ways. For example, with regards to data consistency, reversibility (using Unmasking), or format of the data. This allows for advanced optimization of privacy and utility for a given type of data and use case.
Rules can be centrally managed and reused in order to standardize data privacy protections. They can be conveniently defined both via a no-code UI or via Automation REST APIs, and are assigned via Privitar Policies.
An example of a Rule that transforms data would be a Tokenization Rule that replaces the original value with random characters in the processed dataset. An email address firstname.lastname@example.org might be randomly replaced with a token such as email@example.com.
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