urca 1.1.0

Unified Resource for Cryptographic Arrays

URCA

Unified Resource for Cryptographic Arrays

The URCA project aims to give the vectorized implementations of many cryptographic primitives in order to investigate the statistical properties. Not only, since sometimes it could be useful to study a reduced version of the primitives, the URCA project tries to give the generalised version of the primitives. The project implements vectorised implementations both for CPUs and GPUs, using respectvely NumPy and CuPy.

Pages

Documentation

Guide for the user

Guide for the developer

Examples

Multiple plaintext can be encrypted at once.

>>> import numpy as np
>>> from urca.cpu.blocks.speck import Speck
>>> speck = Speck(32, 64)
>>> word_type = speck.word_type
>>> texts = np.array([[0x6574, 0x694C], [0x0000, 0x0000]], dtype=word_type)
>>> keys = np.array([[0x1918, 0x1110, 0x0908, 0x0100], [0x0000, 0x0000, 0x0000, 0x0000]], dtype=word_type)
>>> speck.encrypt(texts, keys, 0, 22)
>>> np.vectorize(hex)(texts)
array([['0xa868', '0x42f2'],
       ['0x2bb9', '0xc642']], dtype='<U6')

URCA is designed to be as general as possible. The following workflow, encrypting a bunch of texts, can be applied to any primitive.

>>> import random
>>> import numpy as np
>>> from urca.cpu.blocks.speck import Speck
>>> primitive = Speck(32, 64)
>>> word_size = primitive.word_size
>>> word_type = primitive.word_type
>>> n_text_words = primitive.n_text_words
>>> n_key_words = primitive.n_key_words
>>> n_instances = 4
>>> texts = [[random.getrandbits(word_size) for _ in range(n_text_words)] for _ in range(n_instances)]
>>> texts = np.array(texts, dtype=word_type)
>>> keys = [[random.getrandbits(word_size) for _ in range(n_key_words)] for _ in range(n_instances)]
>>> keys = np.array(keys, dtype=word_type)
>>> primitive.encrypt(texts, keys, 0, 22)
>>> np.vectorize(hex)(texts)
# array([['0x3068', '0xc0bf'],
#        ['0xb30b', '0xbed8'],
#        ['0xbb16', '0xece6'],
#        ['0x921a', '0x6f0a']], dtype='<U6')
# Example of output