Introduction
Learn about data privacy.
Anonymization
Protegrity Anonymization, developed by Protegrity, assesses the reidentification risk of datasets containing personal data.
Protegrity Anonymization allows processing of the datasets, via generalization, to ensure the risk of reidentification is within tolerable thresholds. For a meaningful anonymization of a dataset, direct identifiers and quasi-identifiers need to be correctly identified and specified on the configuration of an anonymization job. If direct identifiers and quasi-identifiers are not correctly specified, the risk metrics do not reflect the true risks of reidentification of that anonymized dataset.
About Protegrity Anonymization
Protegrity Anonymization, developed by Protegrity, assesses the reidentification risk of datasets containing personal data.
Installing Protegrity Anonymization
Protegrity Anonymization is available as a REST API that can be installed and run from Kubernetes environments on AWS and Azure. After installing the REST API, you can use Protegrity Anonymization API to anonymize your data. We also offer a local docker deployment mode.
Using Protegrity Anonymization
This section explains the REST APIs provided by Protegrity Anonymization. It also details the method for creating and running Protegrity Anonymization SDK requests.
Building the Anonymization request
Use the APIs provided with the Protegrity Anonymization to create your request.
Using the Auto Anonymizer
The Auto Anonymizer feature of Protegrity Anonymization is a powerful feature for performing anonymization. It processes the data to generate a template for completing anonymization requests.
Using Sample Anonymization Jobs
Sample anonymization jobs that you can use for working with and testing Protegrity Anonymization.
Additional Information
Additional information to help you using the product.
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