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Repository files navigation

ZK-Kit logo JS

Github license GitHub Workflow Tests Coveralls Linter eslint Code style prettier Commitizen friendly

ZK-Kit is a set of libraries (algorithms or utility functions) that can be reused in different projects and zero-knowledge protocols, making it easier for developers to access user-friendly, tested, and documented code for common tasks. ZK-Kit provides different repositories for each language - this one contains JavaScript code only.

β™š Yarn workspaces: minimal monorepo package management (yarn, yarn build, yarn docs)
β™› Conventional Commits: human and machine readable meaning to commit messages (yarn commit)
β™œ Jest: tests and test coverage for all libraries (yarn test:libraries)
β™ž ESLint, Prettier: code quality and formatting (yarn prettier & yarn lint)
♝ Typedocs: documentation generator for TypeScript (yarn docs)
β™Ÿ Benny: simple benchmarking framework for JavaScript/TypeScript (yarn benchmarks)
β™Ÿ Github actions: software workflows for automatic testing, documentation deploy and code quality checks

πŸ—‚οΈ Repositories

πŸ“„ Papers

πŸ“¦ Packages

Package Version Downloads Size Audited
@zk-kit/eddsa-poseidon (docs) NPM version Downloads npm bundle size (scoped) βœ”οΈ
@zk-kit/poseidon-cipher (docs) NPM version Downloads npm bundle size (scoped) ❌
@zk-kit/baby-jubjub (docs) NPM version Downloads npm bundle size (scoped) βœ”οΈ
@zk-kit/utils (docs) NPM version Downloads npm bundle size (scoped) βœ”οΈ
@zk-kit/imt (docs) NPM version Downloads npm bundle size (scoped) ❌
@zk-kit/lean-imt (docs) NPM version Downloads npm bundle size (scoped) βœ”οΈ
@zk-kit/smt (docs) NPM version Downloads npm bundle size (scoped) ❌
@zk-kit/poseidon-proof (docs) NPM version Downloads npm bundle size (scoped) ❌
@zk-kit/logical-expressions (docs) NPM version Downloads npm bundle size (scoped) ❌

πŸ‘₯ Ways to contribute

  • πŸ”§ Work on open issues
  • πŸ“¦ Suggest new packages
  • πŸš€ Share ideas for new features
  • πŸ› Create a report if you find any bugs in the code

πŸ›  Install

Clone this repository:

git clone https://github.com/privacy-scaling-explorations/zk-kit.git

and install the dependencies:

cd zk-kit && yarn

πŸ“œ Usage

Code quality and formatting

Run ESLint to analyze the code and catch bugs:

yarn lint

Run Prettier to check formatting rules:

yarn format

or to automatically format the code:

yarn format:write

Conventional commits

ZK-Kit uses conventional commits. A command line utility to commit using the correct syntax can be used by running:

yarn commit

It will also automatically check that the modified files comply with ESLint and Prettier rules.

Testing

Test the code:

yarn test

Build

Build all the packages:

yarn build

A dist folder will be created inside each JavaScript package.

Documentation

Generate a documentation website for each package:

yarn docs

The output will be placed on the docs folder.

Releases

  1. Bump a new version of the package with:
yarn version:bump <package-name> <version>
# e.g. yarn version:bump utils 2.0.0

This step creates a commit and a git tag.

  1. Push the changes to main:
git push origin main
  1. Push the new git tag:
git push origin <package-name>-<version>
# e.g. git push origin utils-v2.0.0

After pushing the new git tag, a workflow will be triggered and will publish the package on npm and release a new version on Github with its changelogs automatically.

❓ FAQ

I have a library that could be reused in other projects. How can I integrate it on ZK-kit?

ZK-kit provides a set of pre-configured development tools. All you have to deal with is your own code, testing and documentation. To create a package follow these steps:

  1. Fork this repository and clone it (or simply clone it directly if you are a collaborator),
  2. Copy one of our current libraries and update the README.md and package.json files with your package name:
cd zk-kit
cp -r packages/smt packages/my-package
cd packages/my-package && rm -fr node_modules dist
grep -r -l "smt" . | xargs sed -i 's/smt/my-package/'
# Update the remaining description/usage sections, and write your code in the src & tests folders!
  1. Create an issue for your package and open a PR.

How can I create benchmarks for my library?

You can see some examples in the benchmarks folder. All you have to do is create a file that exports a function to run your benchmark in that folder and add that function to the index.ts file. The yarn benchmarks command can be run with no parameters (it will run all the benchmarks), or you can specify the name of your benchmark file to run just that. When you run the command it will create a benchmarks/results folder with your results.

I need to use a Merkle Tree to prove the inclusion or exclusion of data elements within a set. Which type of Merkle Tree should I use?

Incremental: Ideal for applications where you frequently add new elements and need to update the tree efficiently.

Lean Incremental: A more memory-efficient version of the incremental Merkle tree.

Sparse: Particularly useful when you need proof of non-membership.

Type Library Name Main Feature Used by
Incremental @zk-kit/imt Fastest for incremental updates. Semaphore V3, Worldcoin
Lean Incremental @zk-kit/lean-imt Optimized for lightweight environments. Semaphore V4, Zupass
Sparse @zk-kit/smt Handles very large sets efficiently. Iden3

Following benchmarks data of zk-kit Merkle Trees implementations:

8 leafs insert delete update generate proof verify proof
fastest IMT IMT ~ SparseMT LeanIMT LeanIMT IMT
slowest LeanIMT IMT ~ SparseMT IMT SparseMT SparseMT
128 leafs insert delete update generate proof verify proof
fastest IMT SparseMT LeanIMT LeanIMT SparseMT
slowest LeanIMT IMT IMT IMT IMT
1024 leafs insert delete update generate proof verify proof
fastest SparseMT SparseMT LeanIMT LeanIMT SparseMT
slowest LeanIMT IMT IMT IMT IMT

From the benchmark data we can take another criteria to evaluate which Merkle tree should be used:

  • IMT have the best performance for medium and small size insert related operations.
  • LeanIMT have the best performance for all the merkle tree sizes for update and generate proof related operations.
  • Sparse is good for larger data insert, delete and verify proof.