oprf 3.0.0

Oblivious pseudo-random function (OPRF) protocol functionality implementations based on Curve25519 primitives.

Oblivious pseudo-random function (OPRF) protocol functionality implementations based on Curve25519 primitives, including both pure-Python and libsodium-based variants.

Purpose

This library provides data structures and methods for a basic oblivious pseudo-random function (OPRF) protocol. Thanks to the underlying oblivious library, users of this library have the option of relying either on pure Python implementations of cryptographic primitives or on wrappers for libsodium.

Package Installation and Usage

The package is available on PyPI:

python -m pip install oprf

The library can be imported in the usual ways:

import oprf
from oprf import *

Examples

This library makes it possible to concisely prepare binary or string data for masking:

>>> from oprf import data, mask
>>> d = data.hash('abc')

A random mask can be constructed and applied to the data. A number of distinct notations is supported for masking in order to minimize differences between the notation within a protocol definition and its implementation:

>>> m = mask() # Create random mask.
>>> m.mask(d) == m(d) == m * d
True
>>> m(d) == d
False

Mask inversion and unmasking are also supported:

>>> c = m(d)
>>> m.unmask(c) == (~m)(c) == c / m == d
True

Masks can also be constructed deterministically from a bytes-like object or string:

>>> m = mask.hash('123')

Because the classes data and mask are derived from bytes, all methods and other operators supported by bytes objects are supported by data and mask objects:

>>> hex = 'a665a45920422f9d417e4867efdc4fb8a04a1f3fff1fa07e998e86f7f7a27a03'
>>> m = mask.fromhex(hex)
>>> m.hex()
'a665a45920422f9d417e4867efdc4fb8a04a1f3fff1fa07e998e86f7f7a27a03'

In addition, Base64 conversion methods are included to support concise encoding and decoding of data and mask objects:

>>> d.from_base64(d.to_base64()) == d
True
>>> m.from_base64(m.to_base64()) == m
True

Documentation

The documentation can be generated automatically from the source files using Sphinx:

cd docs
python -m pip install -r requirements.txt
sphinx-apidoc -f -E --templatedir=_templates -o _source .. ../setup.py && make html

Testing and Conventions

All unit tests are executed and their coverage is measured when using pytest (see setup.cfg for configuration details):

python -m pip install pytest pytest-cov
python -m pytest

Alternatively, all unit tests are included in the module itself and can be executed using doctest:

python oprf/oprf.py -v

Style conventions are enforced using Pylint:

python -m pip install pylint
python -m pylint oprf

Contributions

In order to contribute to the source code, open an issue or submit a pull request on the GitHub page for this library.

Versioning

The version number format for this library and the changes to the library associated with version number increments conform with Semantic Versioning 2.0.0.

Publishing

This library can be published as a package on PyPI by a package maintainer. Install the wheel package, remove any old build/distribution files, and package the source into a distribution archive:

python -m pip install wheel
rm -rf dist *.egg-info
python setup.py sdist bdist_wheel

Next, install the twine package and upload the package distribution archive to PyPI:

python -m pip install twine
python -m twine upload dist/*