formosa-mldsa

KO | EN

gitlite — search

#python #react #typescript #go #golang #android #deep-learning #macos #nodejs #awesome #vue #pytorch #ios #mcp

(★ 10)

Jasmin implementations of ML-DSA

formosa-mldsa 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

formosa-mldsa
=======

Jasmin implementations of the pure, hedged version of ML-DSA for **x86-64** and **ARMv7M**.

- [Overview](#overview)
- [Quickstart](#quickstart)

## ⚠️ Security and Correctness Notice

This repository is **experimental**.

The code has been **lightly tested only** and has **not been formally
verified or proven** with respect to:

- Memory safety
- Constant-time behavior
- Speculative constant-time behavior
- Functional correctness

Accordingly, **this code MUST NOT be used in production**.

An ongoing effort is underway to conduct comprehensive testing
(e.g., valgrind, KAT, and related tooling) and to formally analyze and
prove the relevant security and correctness properties. Once completed,
the repository will be updated with the corresponding proofs, test
artifacts, and accompanying documentation.

Until then, this implementation should be considered research-grade
software and is provided strictly for evaluation and experimentation
purposes.

## Overview

This repository contains, for all parameter sets, the following implementations of the pure, hedged version of ML-DSA:

### x86-64

1. A reference implementation (found under `x86-64/ref`).
2. An implementation optimized using the AVX2 instruction set (found under `x86-64/avx2`).

### ARMv7M

1. A reference implementation (found under `arm-m4/ref`).
2. An implementation that minimizes use of stack space (found under `arm-m4/lowram`).

## Quickstart

0. Ensure you have the [Jasmin](https://github.com/jasmin-lang/jasmin) compiler installed,
using the latest commit on the `main` branch of the project. Also ensure that `jasminc` is in the PATH, since this is what the Makefile invokes.

1. To generate assembly code for, say, the AVX2 implementation of ML-DSA-65 on the x86-64 platform, run:

```
env ARCHITECTURE=x86-64 PARAMETER_SET=65 IMPLEMENTATION_TYPE=avx2 make
```

The resulting assembly implementation will be found in `ml_dsa_65_avx2_x86-64.s`.

1. Similarly, to generate assembly code for, say, the minimal-stack-usage implementation of ML-DSA-44 on the ARMv7M platform, run:

```
env ARCHITECTURE=arm-m4 PARAMETER_SET=44 IMPLEMENTATION_TYPE=lowram make
```

The resulting assembly implementation will be found in `ml_dsa_44_lowram_arm-m4.s`.

formosa-mldsa

formosa-mldsa 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

formosa-mldsa

formosa-mldsa 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README