electionguard

🗳 ElectionGuard Python

This repository is a "reference implementation" of ElectionGuard written in Python 3. This implementation can be used to conduct End-to-End Verifiable Elections as well as privacy-enhanced risk-limiting audits. Components of this library can also be used to construct "Verifiers" to validate the results of an ElectionGuard election.

📁 In This Repository

File/folder	Description
docs	Documentation for using the library.
src/electionguard	ElectionGuard library.
src/electionguard_tools	Tools for testing and sample data.
src/electionguard_verifier	Verifier to validate the validity of a ballot.
stubs	Type annotations for external libraries.
tests	Tests to exercise this codebase.
CONTRIBUTING.md	Guidelines for contributing.
README.md	This README file.
LICENSE	The license for ElectionGuard-Python.
data	Sample election data.

❓ What Is ElectionGuard?

ElectionGuard is an open source software development kit (SDK) that makes voting more secure, transparent and accessible. The ElectionGuard SDK leverages homomorphic encryption to ensure that votes recorded by electronic systems of any type remain encrypted, secure, and secret. Meanwhile, ElectionGuard also allows verifiable and accurate tallying of ballots by any 3rd party organization without compromising secrecy or security.

Learn More in the ElectionGuard Repository

🦸 How Can I use ElectionGuard?

ElectionGuard supports a variety of use cases. The Primary use case is to generate verifiable end-to-end (E2E) encrypted elections. The ElectionGuard process can also be used for other use cases such as privacy enhanced risk-limiting audits (RLAs).

💻 Requirements

• Python 3.9+ is required to develop this SDK. If developer uses multiple versions of python, pyenv is suggested to assist version management.
• GNU Make is used to simplify the commands and GitHub Actions. This approach is recommended to simplify the command line experience. This is built in for MacOS and Linux. For Windows, setup is simpler with Chocolatey and installing the provided make package. The other Windows option is manually installing make.
• Gmpy2 is used for Arbitrary-precision arithmetic which has its own installation requirements (native C libraries) on Linux and MacOS. ⚠️ Note: This is not required for Windows since the gmpy2 precompiled libraries are provided.
• poetry 1.1.13 is used to configure the python environment. Installation instructions can be found here.

🚀 Quick Start

Using make, the entire GitHub Action workflow can be run with one command:

make

The unit and integration tests can also be run with make:

make test

A complete end-to-end election example can be run independently by executing:

make test-example

For more detailed build and run options, see the documentation.

📄 Documentation

Overviews:

• GitHub Pages
• Read the Docs

Sections:

• Design and Architecture
• Build and Run
• Project Workflow
• Election Manifest

Step-by-Step Process:

• Configure Election
• Key Ceremony
• Encrypt Ballots
• Cast and Spoil
• Decrypt Tally
• Publish and Verify

Contributing

This project encourages community contributions for development, testing, documentation, code review, and performance analysis, etc. For more information on how to contribute, see the contribution guidelines

Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [e-mail] with any additional questions or comments.

Reporting Issues

Please report any bugs, feature requests, or enhancements using the GitHub Issue Tracker. Please do not report any security vulnerabilities using the Issue Tracker. Instead, please report them to the Microsoft Security Response Center (MSRC) at https://msrc.microsoft.com/create-report. See the Security Documentation for more information.

Have Questions?

Electionguard would love for you to ask questions out in the open using GitHub Issues. If you really want to email the ElectionGuard team, reach out at [e-mail].

License

This repository is licensed under the MIT License

Thanks! 🎉

A huge thank you to those who helped to contribute to this project so far, including:

Josh Benaloh (Microsoft)

Keith Fung (InfernoRed Technology)

Matt Wilhelm (InfernoRed Technology)

Dan S. Wallach (Rice University)