Introduction
Learn about data privacy.
Protegrity Anonymization
Removes personal information from datasets, enabling analysis without revealing individual identities.
Protegrity Anonymization is a software solution that removes personal information and transforms data attributes to maintain privacy. It takes raw data as input, applies techniques such as generalization and summarization, and outputs anonymized data. You can use this output for analysis without revealing individual identities.
Understanding the Architecture and Components
Overview of Protegrity Anonymization architecture, processing flow, and deployment components on AWS EKS.
Prerequisites for Deployment
Required tools, AWS permissions, and EKS cluster configuration for deployment.
Server Installation
Step-by-step deployment of Protegrity Anonymization.
Upgrading Protegrity Anonymization
Upgrade from the previous version.
User Creation in PPC
Update PPC role permissions, authenticate with the gateway, and create an Anonymization administrator user for API access.
Using Anon
Install and configure the Python SDK, validate connectivity, and use the API reference for client setup and anonymization operations.
Uninstallation and Cleanup
Remove the Anonymization Helm release, clean up Kubernetes resources, and destroy provisioned AWS infrastructure with OpenTofu.
Troubleshooting
Troubleshoot common issues with Protegrity Anonymization, including pod failures, API errors, and OpenTofu infrastructure problems.
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