Danger

Nothing here should be used for any security purposes.

  • If you need cryptographic tools in a Python environment use pyca.

  • If you need efficient and reliable abstract math utilities in a Python-like environment consider using SageMath.

Toy cryptographic functions and utilities

Some toy (unsafe for actual use) cryptography related utilites.

Installation

Don’t. If you need to do cryptography in Python use pyca.

If you must

pip install toycrypto

Import names

Once installed, the modules are imported under toy_crypto. For example, Number Theory module would be imported with import toy_crypto.nt.

>>> from toy_crypto.nt import factor
>>> n = 69159288649
>>> factorization = factor(n)
>>> factorization.data
[(11, 2), (5483, 1), (104243, 1)]
>>> str(factorization)
'11^2 * 5483 * 104243'
>>> factorization.n == n
True
>>> factorization.phi
62860010840

Note again that the SageMath Factorization class is far more efficient and general than what exists in this toy cryptography module.

Modules

Modules currently documented:

Motiviation

This package is almost certainly not the package you are looking for. Instead, pyca or SageMath will better suite your needs. I created it to meet a number of my own idiosyncratic needs.

  • I don’t have the flexibility of Python version that I may want when using SageMath.

    For example, I want to have access to something that behaves a bit like SageMath’s factor() or the ability to play with elliptic curves without having do everything in Sage. Perhaps when sagemath-standard quickly becomes available for the latest Python versions, I won’t need to have my own (failable and incomplete) pure Python substitutes for some things I need.

  • I sometimes talk about these algorithms for teaching purposes. Having pure Python versions allows me to present these.

    Proper cryptographic packages, like pyca,

    • Correctly obscure the lower level primitives I may wish to exhibit;

    • Correctly prevent use of unsafe parameters such as small keys;

    • Correctly involve a lot of abstractions in the calls to the concealed primitives.

    Those features, essential for something to be used, are not great for expository discussion.

  • Some of these I created or copied for my own learning purposes.

  • I have a number of “good enough” (for my purposes) implementations of things that I want to reuse.

    For example, Birthday collision calculations are things I occasionally want, and I don’t want to hunt for wherever I have something like that written or rewrite it yet again. Likewise, I wouldn’t be surprised if I’m written the extended GCD algorithm more than a dozen times (not all in Python), and so would like to have at least the Python version in one place

  • I want to use cryptographic examples in Jupyter Notebooks.

    I also want them to be reproducible, which is why I am making this public.