Anonymization on

Anonymization Architecture

Tue, 20 Feb 2024 00:00:00 +0000

Protegrity Anonymization allows processing of the datasets through generalization, to ensure the risk of re-identification is within tolerable thresholds. The 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.

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.

Prerequisites for Anonymization

Tue, 20 Feb 2024 00:00:00 +0000

Ensure that the following prerequisites are met before running these examples for Anonymization:

Docker CLI, Docker Compose, and Python are installed. For more information, refer to AI Developer Edition, Pre-requisites Guide.
For shell samples: Bash version greater than or equal to 5.1.8 and curl version greater than or equal to 7.76.1.
For notebook samples: JupyterLab version greater than or equal to 4.5.6.

Setting up Anonymization

Tue, 20 Feb 2024 00:00:00 +0000

Use the containers to set up the Anonymization feature required for identifying sensitive data.

Open a command prompt.
Navigate to the cloned repository location for protegrity-ai-developer-edition.
Run the following command to download and start the containers. The dependent containers are large in size. Based on the network connection, the containers might take time to download and deploy.
```
cd anonymization
docker compose up -d
```
Based on your configuration use the docker-compose up -d command.

Running the Anonymization samples

Tue, 20 Feb 2024 00:00:00 +0000

The example scripts under the anonymization/ folder demonstrate the usage of Anonymization APIs. For more information about the Anonymization APIs, refer to the section Anonymization APIs.

Note: A dedicated anonymization/docker-compose.yml is provided to start the Anonymization services.

Open a command prompt.
Navigate to the directory where AI Developer Edition is cloned.
Run the following command to start Jupyter Lab.
```
jupyter lab
```
Copy the URL displayed and navigate to the site from a web browser. Ensure that localhost is replaced with the IP address of the system where the AI Developer Edition is set up.

Using the Anonymization APIs

Tue, 20 Feb 2024 00:00:00 +0000

client

Anonymization SDK Client.

Provides synchronous (AnonymizationClient) and asynchronous (AsyncAnonymizationClient) Python clients for the Anonymization anonymization API.

Public models, enums, and exceptions are re-exported here for backward compatibility so that from anonymization_sdk.client import X continues to work.

AnonymizationClient

class AnonymizationClient()

Synchronous client for the Anonymization anonymization API.

Arguments:

base_url - Base URL of the Anonymization API (default: http://localhost:8000)
timeout - Request timeout in seconds (default: 30)
headers - Additional headers to include in requests

Uninstalling Anonymization

Tue, 20 Feb 2024 00:00:00 +0000

Open a command prompt.
Navigate to the cloned repository location.
Navigate to the anonymization directory.
```
cd anonymization
```
Run the following command to remove the containers and images.
```
docker compose down --rmi all
```