The General Sieve Kernel (G6K)

G6K is a C++ and Python library that implements several Sieve algorithms to be used in more advanced lattice reduction tasks. It follows the stateful machine framework from:

Martin R. Albrecht and Léo Ducas and Gottfried Herold and Elena Kirshanova and Eamonn W. Postlethwaite and Marc Stevens, The General Sieve Kernel and New Records in Lattice Reduction.

The article is available in this repository and on eprint .

Building the library

You will need the current master of FPyLLL. See bootstrap.sh for creating (almost) all dependencies from scratch:

# once only: creates local python env, builds fplll, fpylll and G6K
./bootstrap.sh [ -j # ]

# for every new shell: activates local python env
source ./activate

On systems with co-existing python2 and 3, you can force a specific version installation using PYTHON=<pythoncmd> ./boostrap.sh instead. The number of parallel compilation jobs can be controlled with -j #.

If building via `./bootstrap.sh` fails, then the script will return an error code. The error codes are documented in `bootstrap.sh.`

Otherwise, you will need fplll and fpylll already installed and build the G6K Cython extension in place like so:

pip install Cython
pip install -r requirements.txt
python setup.py build_ext [ -j # ]

It's possible to alter the C++ kernel build configuration as follows:

make clean
./configure [opts...]           # e.g. opts: --enable-native --enable-templated-dim --with-max-sieving-dim=128
                                # see ./configure --help for more options
python setup.py build_ext [ -j # ]

Tests

python -m pytest

Gathering test coverage

Uncomment the line extra_compile_args += ["-DCYTHON_TRACE=1"] in setup py. and recompile. Then run

py.test --cov=g6k

Reproducing experiments of the paper for the command line

3-sieve (Sec 5.1)

To recreate Figure 2, run (if you have 26 threads, otherwise change --threads and, possibly, decrease the dimension):

python ./full_sieve.py 100 --sieve hk3 --seed 23 --trials 2 --threads 26 --db_size_base 1.140174986570044 1.1414898159861084 1.1428031326523391 1.1441149417781413 1.14542524854309 1.146734058097168 1.1480413755610026 1.1493472060 1.153255825912013 1.154555758722808 1.1547005383

The whole experiment took ~15 h. If you do not want to wait that long, decrease the dimension. Note : Asymptotically, one would need to adjust the saturation_radius accordingly. However, at these dimensions, the default db_size_factor was large enough to accomodate saturation in practce.

Exact-SVP (Sec 6.1)

Before benchmarking for exact-SVP, one must first determine the length of the shortest vector. To do so on 3 lattices in each dimensions d ∈ {50, 52, 54, 56, 58}:

python ./svp_exact_find_norm.py 50 -u 60 --workers 4 --challenge-seed 0 1 2

This will run 4 independent tasks in parrallel, and takes about 1 minute. Challenges will be downloaded from https://www.latticechallenge.org/ if not already present.

Then, run and obtain averaged timing:

python ./svp_exact.py 50 -u 60 --workers 3 --challenge-seed 0 1 2

Which will take around 10 seconds. To compare several algorithms, and average over 5 trials on each of the 3 lattices for d=50, you can run:

python ./svp_exact.py 50 --workers 3 --trials 5 --challenge-seed 0 1 2 --svp/alg workout enum

SVP-challenge (Sec 6.2)

You can here run a single instance on multiple cores, for example:

python ./svp_challenge.py 100 --threads 4

The above may take between half a minute and 10 minutes depending on how lucky you are

BKZ (Sec 6.3)

To recreate the experiments in the paper run:

python bkz.py 180 --bkz/betas 60:95:1 --bkz/pre_beta 59 --trials 8 --workers 8
python bkz.py 180 --bkz/betas 60:93:1 --bkz/pre_beta 59 --trials 8 --workers 8 --bkz/extra_d4f 12
python bkz.py 180 --bkz/betas 60:97:1 --bkz/pre_beta 59 --trials 8 --workers 8 --bkz/extra_d4f 12 --bkz/jump 3
python bkz.py 180 --bkz/betas 60:85:1 --bkz/pre_beta 59 --trials 8 --workers 8 --bkz/alg naive
python bkz.py 180 --bkz/betas 60:82:1 --bkz/pre_beta 59 --trials 8 --workers 8 --bkz/alg fpylll

LWE (Sec 6.4)

To automatically attempt to solve a Darmstadt LWE Challenge (n, alpha) run:

python lwe_challenge.py n --lwe/alpha alpha

Other CLI programs and commands

It is also possible ot ask for HKZ reduction with hkz.py and hkz_maybe.py; the former really tries hard to get a HKZ basis (with no formal guarentees though) while the latter is providing something close to a HKZ basis significantly significantly faster than the former.

Other options: Each of the parameters PARAM listed in g6k/siever_param.pyx can be set-up to a value VAL from the command line

--PARAM VAL

Though some of them may be overwritten by the call chain. A subset of reasonable parameter to play with are:

threads                         # Number of threads collaborating in a single g6k instance. Default=1
sample_by_sums                  # When increasing the db size, do that aggressively by sampling vectors as sums of existing vectors. Default=True
otf_lift                        # Lift vectors on the fly; slower per sieve, but highter probability to find a short vector in the lift context. Default=True
lift_radius                     # Bound (relative to squared-GH) to try to lift a vector on the fly. Default=1.7
saturation_ratio                # Stop the sieve when this ratio of vector has been found compared to the expected number of vector. Default=.5
saturation_radius               # Define the ball square-radius for the saturation_ratio condition. Default=1.333333333
dual_mode                       # Implicitly run all operations on the dual-basis (in reversed order).

Other parameters specific to subprograms SUBPRG∊{pump, workout, bkz} can be set-up to a value VAL form the CLI by adding the option

--SUBPRG/PARAM VAL

One can also specify a set of values, or a range of value, to iterate over

--SUBPRG/PARAM VAL0 VAL1 ... VALx
--SUBPRG/PARAM MIN_VAL~MAX_VAL
--SUBPRG/PARAM MIN_VAL~MAX_VAL~STEP_VAL

One can find all the available option by browsing through the programs in the g6k/algorithms/ subdirectory.

It is also possible to plot or to output the so called `profile', namely the logarithmic plot of the Gram-Schmidt norms, with the option

--profile filename.csv      #exporting raw data as column seperated values
--profile filename.EXT      #for EXT∊{png,pdf,...} plot in a file, requires matplotlib
--profile show              #plot in a pop-up window, requires matplotlib

Interactive use of G6K from Python

General Sieving Kernel. We start by importing the siever and FPYLLL

>>> from fpylll import IntegerMatrix, LLL, FPLLL
>>> from g6k import Siever

Construct a challenge instance

>>> FPLLL.set_random_seed(0x1337)
>>> A = IntegerMatrix.random(50, "qary", k=25, bits=20)
>>> A = LLL.reduction(A)

Construct the instance

>>> g6k = Siever(A)
>>> g6k.initialize_local(0, 0, 50)
>>> g6k(alg="gauss")

We recover the shortest vector found. Best lift returns the index, the squared norm and the vector expressed in base A:

>>> i, norm, coeffs = g6k.best_lifts()[0]
>>> l = int(round(norm))
>>> l < 3710000
True

To test the answer we compute:

>>> v = A.multiply_left(coeffs)
>>> sum(v_**2 for v_ in v) == l
True

More examples can be found in the folder examples.