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Booltest: Polynomial randomness tester

Project description

Booltest

Build Status

Boolean PRNG tester - analysing statistical properties of PRNGs.

Randomness tester based on our paper published at Secrypt 2017

How does it work?

Booltest generates a set of boolean functions, computes the expected result distribution when evaluated on truly random data and compares this to the evaluation on the data being tested.

Pip installation

Booltest is available via pip:

pip install booltest

Local installation

From the local dir:

pip install --upgrade --find-links=. .

The engine

Booltest does the heavy lifting with the native python extension bitarray_ph4

Bitarray operations are performed effectively using fast operations implemented in C.

Experiments

First launch

The following commands generate two different files, random and zero-filled. Both are tested, the difference between files should be evident.

dd if=/dev/urandom of=random-file.bin bs=1024 count=$((1024*10))
dd if=/dev/zero of=zero-file.bin bs=1024 count=$((1024*10))

booltest --degree 2 --block 256 --top 128 --tv $((1024*1024*10)) --rounds 0 random-file.bin
booltest --degree 2 --block 256 --top 128 --tv $((1024*1024*10)) --rounds 0 zero-file.bin

Java random

Analyze output of the java.util.Random, use only polynomials in the specified file. Analyze 100 MB of data:

booltest --degree 2 --block 512 --top 128 --tv $((1024*1024*100)) --rounds 0 \
  --poly-file data/polynomials/polynomials-randjava_seed0.txt \
  randjava_seed0.bin

Reference statistics

In order to test reference statistics of the test we computed polynomial tests on input vectors generated by AES-CTR(SHA256(random_32bit())) - considered as random data source. The randverif.py was used.

The first hypothesis to verify is the following: under null hypothesis (uniform input data), zscore test is input data size invariant. In other words, the zscore result of the test is not influenced by amount of data processed.

To verify the first hypothesis we analyzed 1000 different test vectors of sizes 1 and 10 MB for various settings (block \in {128, 256} x deg \in {1, 2, 3} x comb_deg \in {1, 2, 3}) and compared results. The test was performed with assets/test-aes-size.sh.

Second test is to determine reference zscore value for random data. For this we performed 100 different tests on 10 MB AES input vectors in all test combinations: block \in {128, 256, 384, 512} x deg \in {1, 2, 3} x comb_deg \in {1, 2, 3}.

Aura testbed

Testbed = battery of functions (e.g., ESTREAM, SHA3 candidates, …) tested with various polynomial parameters (e.g., block \in {128, 256, 384, 512} x deg \in {1, 2, 3} x comb_deg \in {1, 2, 3}).

EAcirc generator is invoked during the test to generate output from battery functions. If switch --data-dir is used testbed.py will try to look up output there first.

In order to start EACirc generator you may need to compile it on the machine you want to test on. Instructions for compilation are on the bottom of the page. In order to invoke the generator you need to setup env

module add mpc-0.8.2
module add gmp-4.3.2
module add mpfr-3.0.0
module add cmake-3.6.2
export PATH=~/local/gcc-5.2.0/bin:$PATH
export LD_LIBRARY_PATH=~/local/gcc-5.2.0/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=~/local/gcc-5.2.0/lib64:$LD_LIBRARY_PATH

In order to start testbed.py there is a script assets/aura-para.sh. It performs the env setup, prepares directories, spawns multiple testing processes.

Parallelization is done in a simple way. Each test has an index. This order is randomized and each process from the batch takes the job that belongs to him (e.g. 10 processes, process #5 takes each 5th job). If the ordering is not favorable for in some way (e.g., one process is getting too much heavy jobs - deg3, combdeg 3) just change the seed of the test randomizer.

Result of each test is stored in a separate file.

Standard functions -> batteries

The goal of this experiment is to assess standard test batteries (e.g., NIST, Dieharder, TestU01) how well they perform on the battery of round reduced functions (e.g., ESTREAM, SHA3 candidates, …)

For the testing we use Randomness Testing Toolkit (RTT) from the EACirc project. The testbatteries.py prepares data for functions to test and the main bash script that submits tests to RTT.

python booltest/testbatteries.py --email ph4r05@gmail.com --threads 3 \
    --generator-path ~/eacirc/generator/generator \
    --result-dir ~/_nni/home/ph4r05/testdata/ \
    --data-dir ~/_nni/home/ph4r05/testdata/ \
    --script-data /home/ph4r05/testdata \
    --matrix-size 1 10 100 1000

RandC

Test found distinguishers on RandC for 1000 different random seeds:

python booltest/randverif.py --test-randc \
    --block 384 --deg 2 \
    --tv $((1024*1024*10)) --rounds 0 --tests 1000 \
    --poly-file polynomials-randc-linux.txt \
    > ~/output.txt

In order to generate CSV from the output:

python csvgen.py output.txt > data.csv

Java tests - version

openjdk version "1.8.0_121"
OpenJDK Runtime Environment (build 1.8.0_121-8u121-b13-0ubuntu1.16.04.2-b13)
OpenJDK 64-Bit Server VM (build 25.121-b13, mixed mode)
Ubuntu 16.04.1 LTS (Xenial Xerus)

Egenerator speed benchmark

Table summarizes function & time needed to generate 10 MB of data.

Function Round Time (sec)
AES 4 2.12984800339
ARIRANG 4 9.43074584007
AURORA 5 0.810596942902
BLAKE 3 0.839290142059
Cheetah 7 0.924134969711
CubeHash 3 36.8423719406
DCH 3 3.34326887131
DECIM 7 51.946573019
DynamicSHA 9 1.33032679558
DynamicSHA2 14 1.14816212654
ECHO 4 2.15773296356
Fubuki 4 1.81450080872
Grain 4 67.9190270901
Grostl 5 2.10276603699
Hamsi 3 7.09616398811
Hermes 3 1.46782112122
JH 8 3.51690793037
Keccak 4 1.31340193748
Lesamnta 5 2.08995699883
LEX 5 0.789785861969
Luffa 8 2.70372700691
MD6 11 2.13406395912
Salsa20 4 0.845487833023
SIMD 3 7.54037189484
Tangle 25 1.43553209305
TEA 8 0.981395959854
TSC-4 14 8.33323192596
Twister 9 1.38356399536

Installation

Scipy installation with pip

pip install pyopenssl
pip install pycrypto
pip install git+https://github.com/scipy/scipy.git
pip install --upgrade --find-links=. .

Virtual environment

It is usually recommended to create a new python virtual environment for the project:

virtualenv ~/pyenv
source ~/pyenv/bin/activate
pip install --upgrade pip
pip install --upgrade --find-links=. .

Aura / Aisa on FI MU

module add cmake-3.6.2
module add gcc-4.8.2

Python 2.7.14

Booltest does not work with lower Python version. Use pyenv to install a new Python version. It internally downloads Python sources and installs it to ~/.pyenv.

git clone https://github.com/pyenv/pyenv.git ~/.pyenv
echo 'export PYENV_ROOT="$HOME/.pyenv"' >> ~/.bashrc
echo 'export PATH="$PYENV_ROOT/bin:$PATH"' >> ~/.bashrc
echo 'eval "$(pyenv init -)"' >> ~/.bashrc
exec $SHELL
pyenv install 2.7.14
pyenv local 2.7.14

GCC 5.2

Installing a new GCC with C++ 11 support. http://bakeronit.com/2015/11/04/install_gcc/

wget http://ftp.gnu.org/gnu/gcc/gcc-5.2.0/gcc-5.2.0.tar.bz2
tar -xjvf gcc-5.2.0.tar.bz2

module add mpc-0.8.2
module add gmp-4.3.2
module add mpfr-3.0.0

mkdir -p ~/local/gcc-5.2.0
cd local
mkdir gcc-build  # objdir
cd gcc-build
../../gcc-5.2.0/configure --prefix=~/local/gcc-5.2.0/ --enable-languages=c,c++,fortran,go --disable-multilib
make -j4 # spend a long time
make install

# Add either to ~/.bashrc or just invoke on shell
export PATH=~/local/gcc-5.2.0/bin:$PATH
export LD_LIBRARY_PATH=~/local/gcc-5.2.0/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=~/local/gcc-5.2.0/lib64:$LD_LIBRARY_PATH

Compiling EACirc generator on Aura/Aisa

module add mpc-0.8.2
module add gmp-4.3.2
module add mpfr-3.0.0
module add cmake-3.6.2
export PATH=~/local/gcc-5.2.0/bin:$PATH
export LD_LIBRARY_PATH=~/local/gcc-5.2.0/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=~/local/gcc-5.2.0/lib64:$LD_LIBRARY_PATH

cd ~/eacirc
mkdir -p build && cd build
CC=gcc CXX=g++ cmake ..
make

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