Volara Satya Proof of Contribution

This repository provides proof of contribution using Satya validators for the Volara dataset on the Vana network.

Overview

This template provides a basic structure for building proof tasks that:

1. Read input files from the /input directory.
2. Process the data securely, running any necessary validations to prove the data authentic, unique, high quality, etc.
3. Write proof results to the /output/results.json file in the following format:

{
  "dlp_id": 19,
  "valid": true,
  "score": 0.85,
  "authenticity": 1.0,
  "ownership": 1.0,
  "quality": 0.9,
  "uniqueness": 0.8,
  "attributes": {
    "email_verified": true,
    "total_score": 0.75,
    "score_threshold": 0.83
  }
}

The project is designed to work with Intel TDX (Trust Domain Extensions), providing hardware-level isolation and security guarantees for confidential computing workloads.

Project Structure

• my_proof/: Contains the main proof logic
  • proof.py: Implements the proof generation logic
  • __main__.py: Entry point for the proof execution
  • models/: Data models for the proof system
• demo/: Contains sample input and output for testing
• Dockerfile: Defines the container image for the proof task
• requirements.txt: Python package dependencies

##Getting Started To begin using this project:

1. Fork the repository: Make a copy of this repository in your GitHub account.
2. Clone the repository:

git clone https://github.com/volaradlp/validator.git cd validator 3. Build the Docker image:

docker build -t volara-validator . 4. Run the proof task:

  --volume $(pwd)/input:/input \
  --volume $(pwd)/output:/output \
  --env USER_EMAIL=user@example.com \
  volara-validator```
## Customizing the Proof Logic

The main proof logic is implemented in `my_proof/proof.py`. To customize it, update the `Proof.generate()` function to change how input files are processed.

The proof can be configured using environment variables:

- `USER_EMAIL`: The email address of the data contributor, to verify data ownership

If you want to use a language other than Python, you can modify the Dockerfile to install the necessary dependencies and build the proof task in the desired language.

## Running with Intel TDX

Intel TDX (Trust Domain Extensions) provides hardware-based memory encryption and integrity protection for virtual machines. To run this container in a TDX-enabled environment, follow your infrastructure provider's specific instructions for deploying confidential containers.

Common volume mounts and environment variables:

```docker run --rm \
  --volume /path/to/input:/input \
  --volume /path/to/output:/output \
  --env USER_EMAIL=user@example.com \
  volara-validator

Remember to populate the /input directory with the files you want to process.

Security Features

This template leverages several security features:

1. Hardware-based Isolation: The proof runs inside a TDX-protected environment, isolating it from the rest of the system
2. Input/Output Isolation: Input and output directories are mounted separately, ensuring clear data flow boundaries
3. Minimal Container: Uses a minimal Python base image to reduce attack surface

Development and testinng

Local Testing: Run proof logic locally for development:

python -m unittest discover 2. Linting and Code Style: Use tools like flake8 or black to ensure code consistency:

black my_proof/```
## Contributing

If you have suggestions for improving this template, please open an issue or submit a pull request.
my suggestion and contribution


## License

[MIT License](LICENSE)