Caution

This ML-DSA implementation is conformant with ML-DSA draft standard @ https://doi.org/10.6028/NIST.FIPS.204.ipd. I also try to make it timing leakage free, using dudect (see https://github.com/oreparaz/dudect) -based tests, but be informed that this implementation is not yet audited. If you consider using it in production, be careful !

ML-DSA (formerly known as Dilithium)

Module-Lattice-Based Digital Signature Standard by NIST.

Motivation

ML-DSA is being standardized by NIST as post-quantum secure digital signature algorithm (DSA), which can be used for verifying the authenticity of digital messages, giving recipient party confidence that the message indeed came from the known sender. ML-DSA's security is based on hardness of finding short vectors in lattice i.e. it's a lattice-based Post Quantum Cryptographic (PQC) construction.

ML-DSA offers following three algorithms.

Algorithm	What does it do ?
KeyGen	It takes a 32 -bytes seed, which is used for deterministically computing a ML-DSA keypair i.e. both public key and secret key.
Sign	It takes a 32 -bytes seed, a ML-DSA secret key and a N (>=0) -bytes message as input, producing ML-DSA signature bytes. For default and recommended hedged message signing, one must provide with 32B random seed. For deterministic message signing, one should simply fill seed with 32 zero bytes.
Verify	It takes a ML-DSA public key, N (>=0) -bytes message and ML-DSA signature, returning boolean value, denoting status of successful signature verification operation.

Here I'm maintaining ml-dsa as a C++20 header-only constexpr library, implementing NIST FIPS 204 ML-DSA, supporting ML-DSA-{44, 65, 87} parameter sets, as defined in table 1 of ML-DSA draft standard. For more details on using this library, see below.

Note

Find ML-DSA draft standard @ https://doi.org/10.6028/NIST.FIPS.204.ipd, which you should refer to when understanding intricate details of this implementation.

Prerequisites

A C++ compiler with C++20 standard library such as clang++/ g++.

$ clang++ --version
Ubuntu clang version 17.0.6 (9ubuntu1)
Target: x86_64-pc-linux-gnu
Thread model: posix
InstalledDir: /usr/bin

$ g++ --version
g++ (Ubuntu 14-20240412-0ubuntu1) 14.0.1 20240412 (experimental) [master r14-9935-g67e1433a94f]

System development utilities such as make, cmake.

$ make --version
GNU Make 4.3

$ cmake --version
cmake version 3.28.3

For testing correctness and compatibility of this ML-DSA implementation, you need to globally install google-test library and headers. Follow guide @ https://github.com/google/googletest/tree/main/googletest#standalone-cmake-project, if you don't have it installed.
For benchmarking ML-DSA algorithms, you must have google-benchmark header and library globally installed. I found guide @ https://github.com/google/benchmark#installation helpful.

Note

If you are on a machine running GNU/Linux kernel and you want to obtain CPU cycle count for ML-DSA routines, you should consider building google-benchmark library with libPFM support, following https://gist.github.com/itzmeanjan/05dc3e946f635d00c5e0b21aae6203a7, a step-by-step guide. Find more about libPFM @ https://perfmon2.sourceforge.net.

Tip

Git submodule based dependencies will normally be imported automatically, but in case that doesn't work, you can manually initialize and update them by issuing $ git submodule update --init from inside the root of this repository.

Testing

For ensuring functional correctness of this library implementation of ML-DSA and conformance with the ML-DSA draft standard, issue following command.

Note

ML-DSA Known Answer Tests, living in this directory, are generated following the procedure, described in https://gist.github.com/itzmeanjan/d14afc3866b82119221682f0f3c9822d.

make -j            # Run tests without any sort of sanitizers
make asan_test -j  # Run tests with AddressSanitizer enabled
make ubsan_test -j # Run tests with UndefinedBehaviourSanitizer enabled

PASSED TESTS (12/12):
       3 ms: build/test.out ML_DSA.HintBitPolynomialEncodingDecoding
       4 ms: build/test.out ML_DSA.HashingToABall
       4 ms: build/test.out ML_DSA.PolynomialEncodingDecoding
      13 ms: build/test.out ML_DSA.Power2Round
      42 ms: build/test.out ML_DSA.MakingAndUsingOfHintBits
      78 ms: build/test.out ML_DSA.ML_DSA_44_KeygenSignVerifyFlow
     116 ms: build/test.out ML_DSA.ML_DSA_44_KnownAnswerTests
     123 ms: build/test.out ML_DSA.ML_DSA_87_KeygenSignVerifyFlow
     126 ms: build/test.out ML_DSA.ML_DSA_65_KeygenSignVerifyFlow
     170 ms: build/test.out ML_DSA.ML_DSA_65_KnownAnswerTests
     276 ms: build/test.out ML_DSA.ML_DSA_87_KnownAnswerTests
     767 ms: build/test.out ML_DSA.ArithmeticOverZq

You can run timing leakage tests, using dudect, execute following

Note

dudect is integrated into this library implementation of ML-DSA to find any sort of timing leakages. It checks for constant-timeness of most of the vital internal functions. Though it doesn't check constant-timeness of functions which use uniform rejection sampling, such as expansion of public matrix A or sampling of the vectors s1, s2 or hashing to a ball etc..

# Can only be built and run on x86_64 machine.
make dudect_test_build -j

# Before running the constant-time tests, it's a good idea to put all CPU cores on "performance" mode.
# You may find the guide @ https://github.com/google/benchmark/blob/main/docs/reducing_variance.md helpful.

timeout 10m taskset -c 0 ./build/dudect/test_ml_dsa_44.out
timeout 10m taskset -c 0 ./build/dudect/test_ml_dsa_65.out
timeout 10m taskset -c 0 ./build/dudect/test_ml_dsa_87.out

Tip

dudect documentation says if t statistic is < 10, we're probably good, yes probably. You may want to read dudect documentation @ https://github.com/oreparaz/dudect. Also you might find the original paper @ https://ia.cr/2016/1123 interesting.

...
meas:   48.38 M, max t:   +2.77, max tau: 3.99e-04, (5/tau)^2: 1.57e+08. For the moment, maybe constant time.
meas:   48.48 M, max t:   +2.73, max tau: 3.93e-04, (5/tau)^2: 1.62e+08. For the moment, maybe constant time.
meas:   48.57 M, max t:   +2.76, max tau: 3.96e-04, (5/tau)^2: 1.59e+08. For the moment, maybe constant time.
meas:   48.67 M, max t:   +2.78, max tau: 3.99e-04, (5/tau)^2: 1.57e+08. For the moment, maybe constant time.
meas:   48.76 M, max t:   +2.79, max tau: 3.99e-04, (5/tau)^2: 1.57e+08. For the moment, maybe constant time.
meas:   48.85 M, max t:   +2.78, max tau: 3.97e-04, (5/tau)^2: 1.58e+08. For the moment, maybe constant time.
meas:   48.95 M, max t:   +2.79, max tau: 3.98e-04, (5/tau)^2: 1.58e+08. For the moment, maybe constant time.
meas:   49.05 M, max t:   +2.77, max tau: 3.95e-04, (5/tau)^2: 1.60e+08. For the moment, maybe constant time.
meas:   49.14 M, max t:   +2.69, max tau: 3.84e-04, (5/tau)^2: 1.70e+08. For the moment, maybe constant time.
meas:   49.24 M, max t:   +2.75, max tau: 3.92e-04, (5/tau)^2: 1.62e+08. For the moment, maybe constant time.
meas:   49.33 M, max t:   +2.73, max tau: 3.89e-04, (5/tau)^2: 1.65e+08. For the moment, maybe constant time.
meas:   49.43 M, max t:   +2.76, max tau: 3.93e-04, (5/tau)^2: 1.62e+08. For the moment, maybe constant time.
meas:   49.52 M, max t:   +2.76, max tau: 3.92e-04, (5/tau)^2: 1.63e+08. For the moment, maybe constant time.
meas:   49.62 M, max t:   +2.79, max tau: 3.97e-04, (5/tau)^2: 1.59e+08. For the moment, maybe constant time.
meas:   49.71 M, max t:   +2.78, max tau: 3.94e-04, (5/tau)^2: 1.61e+08. For the moment, maybe constant time.

Benchmarking

Warning

Relying only on average timing measurement for understanding performance characteristics of ML-DSA sign algorithm may not be a good idea, given that it's a post-quantum digital signature scheme of "Fiat-Shamir with Aborts" paradigm - simply put, during signing procedure it may need to abort and restart again, multiple times, based on what message is being signed or what random seed is being used for default hedged signing. So it's a better idea to also compute other statistics such as minimum, maximum and median ( pretty useful ) when timing execution of sign procedure. In following benchmark results, you'll see such statistics demonstrating broader performance characteristics of ML-DSA sign procedure for various parameter sets.

Benchmarking key generation, signing and verification algorithms for various instantiations of ML-DSA can be done, by issuing

make benchmark -j  # If you haven't built google-benchmark library with libPFM support.
make perf -j       # If you have built google-benchmark library with libPFM support.

Caution

Ensure you've put all CPU cores on performance mode, before running benchmarks, follow guide @ https://github.com/google/benchmark/blob/main/docs/reducing_variance.md.

On 12th Gen Intel(R) Core(TM) i7-1260P

Compiled with gcc version 14.0.1 20240412.

$ uname -srm
Linux 6.8.0-39-generic x86_64

2024-08-05T11:18:02+05:30
Running ./build/perf.out
Run on (16 X 400.497 MHz CPU s)
CPU Caches:
  L1 Data 48 KiB (x8)
  L1 Instruction 32 KiB (x8)
  L2 Unified 1280 KiB (x8)
  L3 Unified 18432 KiB (x1)
Load Average: 1.34, 1.24, 1.07
-------------------------------------------------------------------------------------------------
Benchmark                           Time             CPU   Iterations     CYCLES items_per_second
-------------------------------------------------------------------------------------------------
ml_dsa_44_verify/32_mean         62.3 us         62.3 us           32   270.887k       16.0698k/s
ml_dsa_44_verify/32_median       63.1 us         63.1 us           32    270.89k       15.8604k/s
ml_dsa_44_verify/32_stddev       1.60 us         1.60 us           32    624.918        421.113/s
ml_dsa_44_verify/32_cv           2.56 %          2.56 %            32      0.23%            2.62%
ml_dsa_44_verify/32_min          58.2 us         58.2 us           32   269.723k       15.4084k/s
ml_dsa_44_verify/32_max          64.9 us         64.9 us           32   272.841k       17.1813k/s
ml_dsa_87_keygen_mean             163 us          163 us           32   698.496k       6.15064k/s
ml_dsa_87_keygen_median           163 us          163 us           32   698.116k       6.15115k/s
ml_dsa_87_keygen_stddev          5.35 us         5.35 us           32   4.08823k        204.362/s
ml_dsa_87_keygen_cv              3.29 %          3.29 %            32      0.59%            3.32%
ml_dsa_87_keygen_min              150 us          150 us           32   690.372k       5.77973k/s
ml_dsa_87_keygen_max              173 us          173 us           32   706.344k       6.67045k/s
ml_dsa_65_verify/32_mean          103 us          103 us           32   443.358k       9.68297k/s
ml_dsa_65_verify/32_median        103 us          103 us           32   443.689k       9.66634k/s
ml_dsa_65_verify/32_stddev       3.04 us         3.04 us           32   1.28769k        282.649/s
ml_dsa_65_verify/32_cv           2.94 %          2.94 %            32      0.29%            2.92%
ml_dsa_65_verify/32_min          99.0 us         99.0 us           32   440.394k       9.09262k/s
ml_dsa_65_verify/32_max           110 us          110 us           32   445.528k        10.102k/s
ml_dsa_87_sign/32_mean            609 us          609 us           32    2.6577M       2.18941k/s
ml_dsa_87_sign/32_median          610 us          610 us           32   2.66831M       1.64061k/s
ml_dsa_87_sign/32_stddev          325 us          325 us           32   1.39882M       1.18589k/s
ml_dsa_87_sign/32_cv            53.28 %         53.28 %            32     52.63%           54.16%
ml_dsa_87_sign/32_min             243 us          243 us           32   1.09957M        695.404/s
ml_dsa_87_sign/32_max            1438 us         1438 us           32   6.19204M       4.10823k/s
ml_dsa_87_verify/32_mean          169 us          169 us           32   721.896k       5.93997k/s
ml_dsa_87_verify/32_median        169 us          169 us           32   721.902k        5.9345k/s
ml_dsa_87_verify/32_stddev       5.59 us         5.58 us           32   1.45918k        196.633/s
ml_dsa_87_verify/32_cv           3.31 %          3.31 %            32      0.20%            3.31%
ml_dsa_87_verify/32_min           155 us          155 us           32   719.374k       5.57533k/s
ml_dsa_87_verify/32_max           179 us          179 us           32   724.646k       6.45852k/s
ml_dsa_65_sign/32_mean            487 us          487 us           32   2.12482M       3.13727k/s
ml_dsa_65_sign/32_median          372 us          372 us           32   1.60891M       2.69802k/s
ml_dsa_65_sign/32_stddev          384 us          384 us           32   1.70012M       1.81803k/s
ml_dsa_65_sign/32_cv            78.82 %         78.82 %            32     80.01%           57.95%
ml_dsa_65_sign/32_min             162 us          162 us           32   724.192k        606.363/s
ml_dsa_65_sign/32_max            1649 us         1649 us           32   7.44815M       6.16409k/s
ml_dsa_44_sign/32_mean            289 us          289 us           32   1.26354M       4.94307k/s
ml_dsa_44_sign/32_median          202 us          202 us           32   885.986k       4.96339k/s
ml_dsa_44_sign/32_stddev          210 us          210 us           32   908.538k        2.5997k/s
ml_dsa_44_sign/32_cv            72.84 %         72.84 %            32     71.90%           52.59%
ml_dsa_44_sign/32_min             106 us          106 us           32   474.061k        948.065/s
ml_dsa_44_sign/32_max            1055 us         1055 us           32   4.37527M       9.41556k/s
ml_dsa_65_keygen_mean             101 us          101 us           32    433.69k       9.93793k/s
ml_dsa_65_keygen_median          99.6 us         99.6 us           32   433.649k       10.0425k/s
ml_dsa_65_keygen_stddev          3.12 us         3.12 us           32    973.148         303.96/s
ml_dsa_65_keygen_cv              3.10 %          3.09 %            32      0.22%            3.06%
ml_dsa_65_keygen_min             93.8 us         93.8 us           32   431.835k       9.32141k/s
ml_dsa_65_keygen_max              107 us          107 us           32   435.258k       10.6581k/s
ml_dsa_44_keygen_mean            59.4 us         59.4 us           32   255.647k       16.8513k/s
ml_dsa_44_keygen_median          59.8 us         59.8 us           32   255.181k       16.7347k/s
ml_dsa_44_keygen_stddev          1.65 us         1.64 us           32   3.67228k          469.9/s
ml_dsa_44_keygen_cv              2.77 %          2.77 %            32      1.44%            2.79%
ml_dsa_44_keygen_min             56.7 us         56.7 us           32   250.237k       16.1611k/s
ml_dsa_44_keygen_max             61.9 us         61.9 us           32    263.83k       17.6413k/s

Usage

ml-dsa is a header-only C++20 constexpr library, mainly targeting 64 -bit desktop/ server grade platforms, which is also pretty easy to use. Let's see how to get started with it.

Clone ml-dsa repository.

cd

# Multi-step cloning and importing of submodules.
git clone https://github.com/itzmeanjan/ml-dsa.git && pushd ml-dsa && git submodule update --init && popd
# Or do single step cloning and importing of submodules.
git clone https://github.com/itzmeanjan/ml-dsa.git --recurse-submodules
# Or clone and then run tests, which will automatically bring in dependencies.
git clone https://github.com/itzmeanjan/ml-dsa.git && pushd ml-dsa && make -j && popd

Write a program which makes use of ML-DSA-{44, 65, 87} key generation, signing and verification API ( all of these functions and constants, representing how many bytes of memory to allocate for holding seeds, public/ secret key and signature, live under ml_dsa_{44,65,87}:: namespace ), while importing proper header files.

// main.cpp

// In case interested in using ML-DSA-65 or ML-DSA-87 API, import "ml_dsa_65.hpp" or "ml_dsa_87.hpp" 
// and use keygen/ sign/ verify functions living either under `ml_dsa_65::` or `ml_dsa_87::` namespace.
#include "ml_dsa/ml_dsa_44.hpp"
#include "ml_dsa/internals/rng/prng.hpp"

int main() {
    // --- --- --- Key Generation --- --- ---

    std::array<uint8_t, ml_dsa_44::KeygenSeedByteLen> seed{};
    std::array<uint8_t, ml_dsa_44::PubKeyByteLen> pubkey{};
    std::array<uint8_t, ml_dsa_44::SecKeyByteLen> seckey{};

    // PRNG.
    // Be careful, read API documentation in `ml_dsa/internals/rng/prng.hpp` before you consider using it in production.
    ml_dsa_prng::prng_t<128> prng;
    prng.read(seed);

    ml_dsa_44::keygen(seed, pubkey, seckey);

    // --- --- --- Message Signing --- --- ---

    std::array<uint8_t, ml_dsa_44::SigningSeedByteLen> rnd{};
    std::array<uint8_t, ml_dsa_44::SigByteLen> sig{};

    // 32 -bytes randomness, for default and recommended *hedged* message signing.
    prng.read(rnd);
    // For deterministic message signing, uncomment following statement, while commenting above statement.
    // std::fill(rnd.begin(), rnd.end(), 0);

    constexpr size_t msg_byte_len = 32; // message byte length can be >= 0
    std::array<uint8_t, msg_byte_len> msg{};

    // Sample a psuedo-random message, to be signed.
    prng.read(msg);

    ml_dsa_44::sign(rnd, seckey, msg, sig);

    // --- --- --- Signature Verification --- --- ---

    const bool is_valid = ml_dsa_44::verify(pubkey, msg, sig);
    assert(is_valid);

    return 0;
}

Finally compile your program, while letting your compiler know where it can find ml-dsa and its dependency headers.

# Assuming `ml-dsa` was cloned just under $HOME

ML_DSA_HEADERS=~/ml-dsa/include
SHA3_HEADERS=~/ml-dsa/sha3/include

g++ -std=c++20 -Wall -Wextra -pedantic -O3 -march=native -I $ML_DSA_HEADERS -I $SHA3_HEADERS main.cpp

ML-DSA Variant	Namespace	Header
ML-DSA-44 Routines	ml_dsa_44::	include/ml_dsa/ml_dsa_44.hpp
ML-DSA-65 Routines	ml_dsa_65::	include/ml_dsa/ml_dsa_65.hpp
ML-DSA-87 Routines	ml_dsa_87::	include/ml_dsa/ml_dsa_87.hpp

✨

All the functions, in this ML-DSA header-only library, are implemented as constexpr functions. Hence you should be able to evaluate ML-DSA key generation, signing and verification at compile-time itself, given that all inputs are known at compile-time, of course.

I present you with following demonstration program, which generates a ML-DSA-44 keypair, signs a message, producing a ML-DSA-44 signature and finally verifies the signature - all at program compile-time. Notice, the static assertion.

// compile_time_ml_dsa_44.cpp
//
// Compile and run this program with
// $ g++ -std=c++20 -Wall -Wextra -pedantic -fconstexpr-ops-limit=125000000 -I include -I sha3/include compile_time_ml_dsa_44.cpp && ./a.out
// or
// $ clang++ -std=c++20 -Wall -Wextra -pedantic -fconstexpr-steps=19000000 -I include -I sha3/include compile_time_ml_dsa_44.cpp && ./a.out

#include "ml_dsa/ml_dsa_44.hpp"

// Compile-time
//
// - Generation of a new keypair, given seed
// - Signing of a known message
// - Verification of signature
//
// for ML-DSA-44, using KAT no. (1). See kats/ml_dsa_44.kat.
constexpr auto
ml_dsa_44_keygen_sign_verify() -> auto
{
  // 7c9935a0b07694aa0c6d10e4db6b1add2fd81a25ccb148032dcd739936737f2d
  constexpr std::array<uint8_t, ml_dsa_44::KeygenSeedByteLen> ξ = { 124, 153, 53, 160, 176, 118, 148, 170, 12, 109, 16,  228, 219, 107, 26,  221, 47,  216, 26, 37,  204, 177, 72,  3,   45, 205, 115, 153, 54,  115, 127, 45 };
  // 0000000000000000000000000000000000000000000000000000000000000000
  constexpr std::array<uint8_t, ml_dsa_44::SigningSeedByteLen> rnd{};
  // d81c4d8d734fcbfbeade3d3f8a039faa2a2c9957e835ad55b22e75bf57bb556ac8
  constexpr std::array<uint8_t, 33> msg = { 216, 28,  77, 141, 115, 79,  203, 251, 234, 222, 61,  63, 138, 3,  159, 170, 42, 44,  153, 87, 232, 53,  173, 85,  178, 46,  117, 191, 87, 187, 85, 106, 200 };

  std::array<uint8_t, ml_dsa_44::PubKeyByteLen> pkey{};
  std::array<uint8_t, ml_dsa_44::SecKeyByteLen> skey{};
  std::array<uint8_t, ml_dsa_44::SigByteLen> sig{};

  ml_dsa_44::keygen(ξ, pkey, skey);
  ml_dsa_44::sign(rnd, skey, msg, sig);
  return ml_dsa_44::verify(pkey, msg, sig);
}

int
main()
{
  // Notice static_assert, yay !
  static_assert(ml_dsa_44_keygen_sign_verify() == true, "Must be able to generate a new keypair, sign a message and verify the signature at program compile-time !");
  return 0;
}

See example program, which demonstrates how to use ML-DSA-44 API, similarly you can use ML-DSA-{65, 87} API.

$ g++ -std=c++20 -Wall -Wextra -pedantic -O3 -march=native -I ./include -I ./sha3/include examples/ml_dsa_44.cpp && ./a.out
ML-DSA-44 @ NIST security level 2
Seed      : afc6c351c70775e04b4ece579e72400afbb31fe8bad3d1d8ed0ba40526b0d528
Pubkey    : 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
Seckey    : 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
Message   : 8a556fe4a5e29a37e80f2ad8f3f8679f1cd3f22b1532bd171373f76aa1402158
Signature : 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
Verified   : true

Caution

Before you consider using Psuedo Random Number Generator which comes with this library implementation, I strongly advice you to go through include/ml_dsa/internals/rng/prng.hpp.

Note

Looking at the API documentation, in header files i.e. include/ml_dsa/ml_dsa_{44,65,87}.hpp, can give you a good idea of how to use ML-DSA API. Note, this library doesn't expose any raw pointer -based interface, rather almost everything is wrapped under statically defined std::span - which one can easily create from std::{array, vector}. I opt for using statically defined std::span -based function interfaces, because we always know, at compile-time, how many bytes the seeds/ keys/ signatures are, for various different ML-DSA instantiations. This gives much better type safety and compile-time error reporting.

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README.md

README.md

ML-DSA (formerly known as Dilithium)

Motivation

Prerequisites

Testing

Benchmarking

On 12th Gen Intel(R) Core(TM) i7-1260P

Usage

Files

README.md

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README.md

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ML-DSA (formerly known as Dilithium)

Motivation

Prerequisites

Testing

Benchmarking

On 12th Gen Intel(R) Core(TM) i7-1260P

Usage