fplll
fplll contains implementations of several lattice algorithms. The implementation relies on floating-point orthogonalization, and LLL [LLL82] is central to the code, hence the name.
It includes implementations of floating-point LLL reduction algorithms [NS09,MSV09], offering different speed/guarantees ratios. It contains a 'wrapper' choosing the estimated best sequence of variants in order to provide a guaranteed output as fast as possible [S09]. In the case of the wrapper, the succession of variants is oblivious to the user.
It includes an implementation of the BKZ reduction algorithm [SE94], including the BKZ-2.0 [CN11] improvements (extreme enumeration pruning, pre-processing of blocks, early termination). Additionally, Slide reduction [GN08] and self dual BKZ [MW16] are supported.
It also includes a floating-point implementation of the Kannan-Fincke-Pohst algorithm [K83,FP85] that finds a shortest non-zero lattice vector. Finally, it contains a variant of the enumeration algorithm that computes a lattice vector closest to a given vector belonging to the real span of the lattice.
fplll is distributed under the GNU Lesser General Public License (either version 2.1 of the License, or, at your option, any later version) as published by the Free Software Foundation.
How to cite
@unpublished{fplll,
author = {The {FPLLL} development team},
title = {{fplll}, a lattice reduction library, {Version}: 5.4.2},
year = 2022,
note = {Available at \url{https://github.com/fplll/fplll}},
url = {https://github.com/fplll/fplll}
}
Table of contents
- fplll
- Table of contents
- Compilation
- How to use
- Programs latticegen, fplll, llldiff.
- How to use as a library
- Multicore support
- Examples
- Alternative interfaces
- Credit
- Contributing
- New releases and bug reports
- Bibliography
Compilation
Installation from packages
fplll is available as a pre-built package for a variety of operating systems; these pre-built packages typically include all mandatory dependencies, and so these packages can be used to start running fplll quickly.
Below, we give some instructions on how to install these packaged variants of fplll.
Note that these packages will be up-to-date for the most recent version of fplll. However, if you want a feature that has recently been added to master (that is not yet in a release) then it is necessary to build from source. If this is the case, please see the Installation from Source section.
Ubuntu and Debian
fplll can be installed directly via Aptitude or Synaptic. Both of these package managers package fplll in the package fplll-tools
. Therefore, to install this package using Aptitude, run the following command
sudo aptitude install fplll-tools
If you want to use Synaptic, then you will need to search for the fplll-tools
package using the search bar.
Conda
fplll can be installed natively as a conda package using the following command
conda install fplll
MacOS
MacOS has a package for fplll inside HomeBrew. Assuming that you have HomeBrew installed, you may install fplll using the following command
brew install fplll
Docker and AWS
We now have Docker/AWS images for fplll too. They aren't on this repository, though; you can find them here
Installation from source
fplll can also be built from source. Below, we explicate some of the dependencies for building from source, as well as operating systems specific instructions.
Dependencies
Required
- GNU MP 4.2.0 or higher http://gmplib.org/ or MPIR 1.0.0 or higher http://mpir.org
- MPFR 2.3.0 or higher, COMPLETE INSTALLATION http://www.mpfr.org/
- autotools 2.61 or higher
- g++ 4.9.3 or higher
Optional
- QD 2.3.15 or higher (a C++/Fortran-90 double-double and quad-double package), compile and install
the shared library (e.g.
./configure --enable-shared=yes
). http://crd-legacy.lbl.gov/~dhbailey/mpdist/
NOTE: If you are intending to use fplll on Windows 10, then these packages should be installed after the Windows Subsystem for Linux
has been installed and activated. Please go to the Windows 10 instructions for more information.
Linux and MacOS
You should download the source code from Github and then run
./autogen.sh
which generates the ./configure
script used to configure fplll by calling the appropriate
autotools command.
Then, to compile and install type
./configure
make
make install # (as root)
If GMP, MPFR and/or MPIR are not in the $LD_LIBRARY_PATH
, you have to point to the directories where the libraries are, with
./configure --with-gmp=path/to/gmp
or
./configure --with-mpfr=path/to/mpfr
The same philosophy applies to the (optional) QD library. If you want to use
mpir instead of gmp, use --enable-mpir
and --with-mpir=path/to/mpir
.
You can remove the program binaries and object files from the source code directory by typing make clean
. To also remove the files that ./configure
created (so you can compile the package for a
different kind of computer), type make distclean
. By default, make install
installs the package
commands under /usr/local/bin
, include files under /usr/local/include
, etc. You can specify an
installation directory name other than /usr/local
by giving ./configure
the option
--prefix=dirname
. Run ./configure --help
for further details.
Windows 10
Windows 10 has the "Windows Subsystem for Linux"(WSL), which essentially allows you to use Linux features in Windows without the need for a dual-boot system or a virtual machine. To activate this, first go to Settings -> Update and security -> For developers and enable developer mode. (This may take a while.) Afterwards, open Powershell as an administrator and run
Enable-WindowsOptionalFeature -Online -FeatureName Microsoft-Windows-Subsystem-Linux
This will enable the WSL. Next, open the Windows Store and navigate to your favourite available Linux distribution - this may be installed as if it were a regular application. Afterwards, this system will act as a regular program, and so it can be opened however you like e.g. by opening command prompt and typing bash
. With this Linux-like subsystem, installing fplll is then similar to above, except that most likely the package repository is not up to date, and various additional packages need to be installed first. To make sure you only install the most recent software, run:
sudo apt-get update
Then run sudo apt-get install <packages>
for the (indirectly) required packages, such as make
, autoconf
, libtool
, gcc
, g++
, libgmp-dev
, libmpfr-dev
and pkg-config
. Finally, download the fplll source code, extract the contents, navigate to this folder in Bash (commonly found under /mnt/c/<local path>
when stored somewhere on the C:\
drive), and run:
./autogen.sh
./configure
make
The same comments as before apply for using e.g. make install
or make distclean
instead of make
.
Note: to fix a potential error libfplll.so.5: cannot open shared object file: No such file or directory
raised after trying to run fplll
after a successful compilation, find the location of libfplll.so.5
(probably something like /../fplll/.libs/
; run find -name libfplll.so.5
to find it) and run export LD_LIBRARY_PATH=<path>
.
Check
Type
make check
Optimization
The default compilation flag is -O3
. One may use the -march=native -O3
flag to optimize the binaries. See "this issue" for its impact on the enumeration speed.
How to use
Executable files fplll
and latticegen
are installed in the directory
/usr/local/bin
. (Note that the programs generated by make
in the fplll/
directory are only wrappers to the programs in fplll/.libs/
).
If you type make check
, it will also generate the executable file llldiff
,
in fplll/.libs/
.
latticegen
latticegen
is a utility for generating matrices (rows form input
lattice basis vectors).
The options are:
r
d
b
: generates a knapsack like matrix of dimension d x (d+1) and b bits (see, e.g., [S09]): the i-th vector starts with a random integer of bit-length <=b and the rest is the i-th canonical unit vector.s
d
b
b2
: generates a d x d matrix of a form similar to that is involved when trying to find rational approximations to reals with the same small denominator (see, e.g., [LLL82]): the first vector starts with a random integer of bit-length <=b2 and continues with d-1 independent integers of bit-lengths <=b; the i-th vector for i>1 is the i-th canonical unit vector scaled by a factor 2^b.u
d
b
: generates a d x d matrix whose entries are independent integers of bit-lengths <=b.n
d
b
c
: generates an ntru-like matrix. If char is 'b', then it first samples an integer q of bit-length <=b, whereas if char is 'q', then it sets q to the provided value. Then it samples a uniform h in the ring Z_q[x]/(x^n-1). It finally returns the 2 x 2 block matrix [[I, Rot(h)], [0, q*I]], where each block is d x d, the first row of Rot(h) is the coefficient vector of h, and the i-th row of Rot(h) is the shift of the (i-1)-th (with last entry put back in first position), for all i>1. Warning: this does not produce a genuine ntru lattice with h a genuine public key (see [HPS98]).N
d
b
c
: as the previous option, except that the contructed matrix is [[q*I, 0], [Rot(h), I]].q
d
k
b
c
: generates a q-ary matrix. If char is 'b', then it first samples an integer q of bit-length <=b; if char is 'p', it does the same and updates q to the smallest (probabilistic) prime that is greater; if char is 'q', then it sets q to the provided value. It returns a 2 x 2 block matrix [[I, H], [0, q*I]], where H is (d-k) x k and uniformly random modulo q. These bases correspond to the SIS/LWE q-ary lattices (see [MR09]). Goldstein-Mayer lattices correspond to k=1 and q prime (see [GM03]).t
d
f
: generates a d x d lower-triangular matrix B with B_ii = 2^(d-i+1)^f for all i, and B_ij is uniform between -B_jj/2 and B_jj/2 for all j<i.T
d
: also takes as input a d-dimensional vector vec read from a file. It generates a d x d lower-triangular matrix B with B_ii = vec[i] for all i and B_ij is uniform between -B_jj/2 and B_jj/2 for all j<i.
The generated matrix is printed in stdout.
Note that by default, the random bits always use the same seed, to ensure reproducibility. The seed may be changed with the option -randseed <integer>
or by using the current time (in seconds) -randseed time
. If you use this option, it must be the first one on the command line.
fplll
fplll
does LLL, BKZ, HKZ or SVP on a matrix (considered as a set of row
vectors) given in stdin or in a file as parameter.
The options are:
-
-a lll
: LLL-reduction (default). -
-a bkz
: BKZ-reduction. -
-a hkz
: HKZ-reduction. -
-a svp
: prints a shortest non-zero vector of the lattice. -
-a sdb
: self dual variant of BKZ-reduction. -
-a sld
: slide reduction. -
-a cvp
: prints the vector in the lattice closest to the input vector. -
-v
: verbose mode. -
-nolll
: does not apply to LLL-reduction. In the case of bkz, hkz and svp, by default, the input basis is LLL-reduced before anything else. This option allows to remove that initial LLL-reduction (note that other calls to LLL-reduction may occur during the execution). In the cas of hlll, verify if the input basis is HLLL-reduced. -
-a hlll
: HLLL-reduction.
Options for LLL-reduction:
-
-d delta
: δ (default=0.99) -
-e eta
: η (default=0.51). See [NS09] for the definition of (δ,η)-LLL-reduced bases. -
-l lovasz
: if !=0 Lovasz's condition. Otherwise, Siegel's condition (default: Lovasz). See [A02] for the definition of Siegel condition. -
-f mpfr
: sets the floating-point type to MPFR (default ifm=proved
). -
-p precision
: precision of the floating-point arithmetic, works only with-f mpfr
. -
-f dd
: sets the floating-point type to double-double. -
-f qd
: sets the floating-point type to quad-double. -
-f dpe
: sets the floating-point type to DPE (default ifm=heuristic
). -
-f double
: sets the floating-point type to double (default ifm=fast
). -
-f longdouble
: sets the floating-point type to long double. -
-z mpz
: sets the integer type to mpz, the integer type of GMP (default). -
-z int
: sets the integer type to int. -
-z long
: as-z int
. -
-z double
: sets the integer type to double. -
-m wrapper
: uses the wrapper. (default ifz=mpz
). -
-m fast
: uses the fast method, works only with-f double
. -
-m heuristic
: uses the heuristic method. -
-m proved
: uses the proved version of the algorithm. -
-y
: early reduction.
With the wrapper or the proved version, it is guaranteed that the basis is LLL-reduced with δ'=2×δ-1 and η'=2×η-1/2. For instance, with the default options, it is guaranteed that the basis is (0.98,0.52)-LLL-reduced.
Options for BKZ-reduction:
-
-b block_size
: block size, mandatory, between 2 and the number of vectors. -
-f float_type
: same as LLL (-p
is required iffloat_type=mpfr
). -
-p precision
: precision of the floating-point arithmetic with-f mpfr
. -
-bkzmaxloops loops
: maximum number of full loop iterations. -
-bkzmaxtime time
: stops aftertime
seconds (up to completion of the current loop iteration). -
-bkzautoabort
: stops when the average slope of the log ||b_i*||'s does not decrease fast enough.
Without any of the last three options, BKZ runs until no block has been updated for a full loop iteration.
-
-s filename.json
: use strategies for preprocessing and pruning paramater (/strategies/default.json provided). Experimental. -
-bkzghbound factor
: multiplies the Gaussian heuristic byfactor
(of float type) to set the enumeration radius of the SVP calls. -
-bkzboundedlll
: restricts the LLL call before considering a block to vector indices within that block. -
-bkzdumgso file_name
: dumps the log ||b_i*|| 's in specified file.
Output formats:
-of
: prints new line (if-a [lll|bkz]
)-of b
: prints the basis (if-a [lll|bkz]
, this value by default)-of bk
: prints the basis (if-a [lll|bkz]
, format compatible with sage)-of c
: prints the closest vector (if-a cvp
, this value by default)-of s
: prints the closest vector (if-a svp
, this value by default)-of t
: prints status (if-a [lll|bkz|cvp|svp]
)-of u
: prints unimodular matrix (if-a [lll|bkz]
)-of uk
: prints unimodular matrix (if-a [lll|bkz]
, format compatible with sage)-of v
: prints inverse of u (if-a lll
)-of vk
: prints inverse of u (if-a lll
, format compatible with sage)
A combination of these option is allowed (e.g., -of bkut
).
Only for -a hlll
:
-t theta
: θ (default=0.001). See [MSV09] for the definition of (δ,η,θ)-HLLL-reduced bases.-c c
: constant for HLLL during the size-reduction (only used iffplll
is compiled with-DHOUSEHOLDER_USE_SIZE_REDUCTION_TEST
)
llldiff
llldiff
compares two bases (b1,...,bd) and (c1,...c_d'): they are considered
equal iff d=d' and for any i, bi = +- ci. Concretely, if basis B is in file 'B.txt' and if basis C is in file 'C.txt' (in the fplll format), then one may run cat B.txt C.txt | ./llldiff
.
How to use as a library
See API documentation and tests as a source of examples.
Multicore support
This library does not currently use multiple cores and running multiple threads working on the same object IntegerMatrix
, LLLReduction
, MatGSO
etc. is not supported. Running multiple threads working on different objects, however, is supported. That is, there are no global variables and it is safe to e.g. reduce several lattices in parallel in the same process.
As an exception to the above, fplll has an implementation of parallel lattice point enumeration. To enable this implementation, make sure you compile with the maximum parallel enumeration dimension greater than 0. Note that increasing this value will increase the compile-time due to the use of templates.
At present fplll does not contain strategies for multi-core pruned enumeration, and so speedups for pruned enumeration may be sub-linear (see this for more information). On the other hand, unpruned enumeration appears to scale linearly.
Examples
-
LLL reduction
./latticegen r 10 1000 | ./fplll
-
Fileinput for reduction. If the file
matrix
contains[[ 10 11] [11 12]]
then
./fplll matrix
produces
[[0 1 ] [1 0 ] ]
-
Random generator
./latticegen -randseed 1234 r 10 1000 | ./fplll ./latticegen -randseed time u 10 16 | ./fplll
-
Solving SVP
./latticegen r 30 3000 | ./fplll -a svp
-
Solving CVP
echo "[[17 42 4][50 75 108][11 47 33]][100 101 102]" | ./fplll -a cvp
Alternative interfaces
- fpylll is a stand-alone Python interface for fplll.
- fplll is included in Sage, see documentation for IntegerMatrix and IntegerLattice.
Credit
Maintainers
fplll is currently maintained by:
- Martin Albrecht, martinralbrecht@googlemail.com
- Shi Bai, shih.bai@gmail.com
Contributors
The following people have contributed to fplll:
- Martin Albrecht
- Shi Bai
- Guillaume Bonnoron
- David Cade
- Léo Ducas
- Joop van de Pol
- Xavier Pujol
- Damien Stehlé
- Marc Stevens
- Gilles Villard
- Michael Walter
Please add yourself here if you make a contribution.
Acknowledgments
-
Patrick Pelissier and Paul Zimmermann for
dpe
. -
David H. Bailey for
QD
. -
Sylvain Chevillard, Christoph Lauter and Gilles Villard for the
configure/make/make install
packaging. -
Timothy Abbott, Michael Abshoff, Bill Allombert, John Cannon, Sylvain Chevillard, Julien Clement, Andreas Enge, Jean-Pierre Flori, Laurent Fousse, Guillaume Hanrot, Jens Hermans, Jerry James, Christoph Lauter, Tancrède Lepoint, Andrew Novocin, Willem Jan Palenstijn, Patrick Pelissier, Julien Puydt, Michael Schneider, Thiemo Seufer, Allan Steel, Gilles Villard and Paul Zimmermann for their support and for many suggestions that helped debugging and improving this code.
-
CONTRIBUTING.md is taken, almost verbatim, from https://github.com/pydanny/djangopackages/blob/master/docs/contributing.rst
-
json.hpp is taken from https://github.com/nlohmann/json
-
This project has been supported by ERC Starting Grant ERC-2013-StG-335086-LATTAC, by the European Union PROMETHEUS project (Horizon 2020 Research and Innovation Program, grant 780701), by EPSRC grant EP/P009417/1 and by EPSRC grant EP/S020330/1.
Contributing
fplll welcomes contributions. See CONTRIBUTING.md for details.
New releases and bug reports
New releases will be announced on https://groups.google.com/forum/#!forum/fplll-devel.
Bug reports may be sent to https://groups.google.com/forum/#!forum/fplll-devel or via https://github.com/fplll/fplll/issues.
Bibliography
[A02] A. Akhavi. Random lattices, threshold phenomena and efficient reduction algorithms. Theor. Comput. Sci. 287(2): 359-385 (2002)
[Chen13] Y. Chen, Lattice reduction and concrete security of fully homomorphic encryption.
[CN11] Y. Chen and P. Q. Nguyen. BKZ 2.0: Better Lattice Security Estimates. ASIACRYPT 2011: 1-20
[GM03] D. Goldstein and A. Mayer. On the equidistribution of Hecke points. Forum Mathematicum, 15:165–189 (2003)
[GN08] N. Gama and P. Q. Nguyen. Finding Short Lattice Vectors within Mordell's Inequality. STOC 2008: 207-216
[GNR13] N. Gama, P. Q. Nguyen and Oded Regev. Lattice Enumeration Using Extreme Pruning.
[HPS98] J. Hoffstein, J. Pipher, J. H. Silverman. NTRU: A Ring-Based Public Key Cryptosystem. ANTS 1998: 267-288
[K83] R. Kannan. Improved algorithms for integer programming and related lattice problems. STOC 1983, 99-108
[FP85] U. Fincke and M. Pohst. Improved methods for calculating vectors of short length in a lattice, including a complexity analysis. Math. Comp., 44(170):463–471 (1985)
[LLL82] A. K. Lenstra, H. W. Lenstra, Jr. and L. Lovasz. Factoring polynomials with rational coefficients. Math. Ann., 261: 515–534 (1982)
[MSV09] I. Morel, D. Stehle and G. Villard. H-LLL: using Householder inside LLL. ISSAC 2009: 271-278
[MW16] D. Micciancio and M. Walter. Practical, Predictable Lattice Basis Reduction. EUROCRYPT 2016: 820-849
[MR09] D. Micciancio and O. Regev. Post-Quantum Cryptography. Chapter of Lattice-based Cryptography, 147-191 (2009)
[NS09] P. Q. Nguyen and D. Stehle. An LLL Algorithm with Quadratic Complexity. SIAM J. Comput. 39(3): 874-903 (2009)
[S09] D. Stehle. Floating-Point LLL: Theoretical and Practical Aspects. The LLL Algorithm 2009: 179-213
[SE94]: C.-P. Schnorr and M. Euchner. Lattice basis reduction: Improved practical algorithms and solving subset sum problems. Math. Program. 66: 181-199 (1994)