About Protegrity Anonymization

Protegrity Anonymization allows processing of the datasets via generalization, to ensure the risk of reidentification is within tolerable thresholds. An example of this generalization process is that instead of a data subject being 32 years old, the Protegrity Anonymization process might need to generalize age to be a range between 30-35 years old. The Protegrity Anonymization process will have an impact on data utility, but Protegrity Anonymization optimizes this fundamental privacy-utility trade-off to ensure maximum data quality within the privacy goals. This trade-off can be further optimized via the importance parameter, later described.

Protegrity Anonymization leverages Kubernetes for data anonymization at scale and it provides instructions and support for deployment and usage on AWS EKS and Microsoft Azure AKS.

Note: Currently, Protegrity Anonymization has been tested only on AWS EKS and Microsoft Azure AKS.