In today’s data-driven world, ensuring individuals’ anonymity is a significant concern for organizations handling sensitive information. This is where the BMW Anonymization API comes into play. This tool not only localizes but also obfuscates sensitive information in images and videos, protecting individual privacy. Let’s dive into how to implement it effectively!

Key Features of the Anonymization API

• Agnostic in Localization Techniques: It supports Semantic Segmentation and Object Detection.
• Modular Sensitive Information: Users can train a Deep Learning model for specific detections such as faces or vehicles.
• Scalable Anonymization Techniques: Supports pixelating, blurring, and blackening, with a recommendation for the highest privacy level using degree 1 blackening.
• DL-Based Model Support: Compatible with models optimized via the Intel OpenVINO toolkit.
• Integration with BMW DL Tools: Ensures compatibility with existing BMW Deep Learning models.

Understanding the General Architecture

The Anonymization API processes an image along with a JSON object that specifies:

• The sensitive information you want to obfuscate.
• The chosen anonymization technique.
• The degree of anonymization.
• The localization technique.

Think of it like sending a letter with sensitive information inside. You can choose what type of envelope to use (localization technique), how strong the seal should be (anonymization degree), and what kind of message you’re sending (sensitive information).

Deployment Options

You can deploy the Anonymization API in two main ways:

• Standalone Docker Container: This is a single instance that connects to other inference APIs.
• Docker Network: A collection of containers running together on the same machine, which allows for flexible resource sharing.

Prerequisites

• Docker
• Docker Compose

Checking & Installing Prerequisites

Check your Docker installation using:

docker --version

Check your Docker Compose installation using:

docker-compose --version

To install Docker and Docker Compose, visit the relevant links:

• Install Docker on Ubuntu
• Install Docker on Windows

Building the Docker Image

To build the project, execute the following command from the project root directory:

docker build -t anonymization_api -f docker/dockerfile .

Running the Docker Container

To run the API on a Linux machine, navigate to the API’s directory and use:

sudo docker run -it -v $(pwd)/src/main:main -v $(pwd)/jsonFiles:jsonFiles -p port_of_your_choice:4343 anonymization_api

On Windows, use:

docker run -it -v $PWD/src/main:main -v $PWD/jsonFiles:jsonFiles -p port_of_your_choice:4343 anonymization_api

API Endpoints

To view all available endpoints, navigate to:

API Documentation

Troubleshooting

You might run into errors, particularly related to URL connectivity or resource allocation. If you face a problem regarding unreachable URLs, a straightforward solution is to clear your configuration file:

urls: [ ]

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Conclusion

Implementing the BMW Anonymization API can significantly enhance the privacy of sensitive data in images and videos. With its modular approach and scalability, this API is tailored for a range of applications needing data anonymity.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.