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encrypt/decrypt examples #98

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731ea73
encrypt_decrypt.ipynb first version
pbelevich Dec 12, 2020
776aa45
Comment decrypt until #95 is available
pbelevich Dec 12, 2020
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315 changes: 315 additions & 0 deletions notebooks/encrypt_decrypt.ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Encryption examples.ipynb",
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/pbelevich/csprng/blob/encrypt_decrypt_notebook/notebooks/encrypt_decrypt.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4JG-7IJgz_dK"
},
"source": [
"# PyTorch/CSPRNG encrypt/decrypt examples"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "H8TZemj30JvQ"
},
"source": [
"torchcsprng 0.2.0 exposes new API for tensor encryption/decryption. Tensor encryption/decryption API is dtype agnostic, so a tensor of any dtype can be encrypted and the result can be stored to a tensor of any dtype. An encryption key also can be a tensor of any dtype. Currently torchcsprng supports AES cipher with 128-bit key in two modes: ECB and CTR."
]
},
{
"cell_type": "code",
"metadata": {
"id": "jC1O-C25vI0W",
"outputId": "beee40f0-7fa8-4354-affd-2fcf18305685",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"# !pip install --pre torchcsprng -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html\n",
"# for CUDA runtime:\n",
"!pip install --pre torchcsprng -f https://download.pytorch.org/whl/nightly/cu101/torch_nightly.html"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Looking in links: https://download.pytorch.org/whl/nightly/cu101/torch_nightly.html\n",
"Collecting torchcsprng\n",
"\u001b[?25l Downloading https://download.pytorch.org/whl/nightly/cu101/torchcsprng-0.2.0.dev20201210%2Bcu101-cp36-cp36m-linux_x86_64.whl (4.4MB)\n",
"\u001b[K |████████████████████████████████| 4.4MB 552kB/s \n",
"\u001b[?25hCollecting torch==1.8.0.dev20201210+cu101\n",
"\u001b[?25l Downloading https://download.pytorch.org/whl/nightly/cu101/torch-1.8.0.dev20201210%2Bcu101-cp36-cp36m-linux_x86_64.whl (788.6MB)\n",
"\u001b[K |████████████████████████████████| 788.6MB 25kB/s \n",
"\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.6/dist-packages (from torch==1.8.0.dev20201210+cu101->torchcsprng) (3.7.4.3)\n",
"Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from torch==1.8.0.dev20201210+cu101->torchcsprng) (0.8)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch==1.8.0.dev20201210+cu101->torchcsprng) (1.18.5)\n",
"\u001b[31mERROR: torchvision 0.8.1+cu101 has requirement torch==1.7.0, but you'll have torch 1.8.0.dev20201210+cu101 which is incompatible.\u001b[0m\n",
"Installing collected packages: torch, torchcsprng\n",
" Found existing installation: torch 1.7.0+cu101\n",
" Uninstalling torch-1.7.0+cu101:\n",
" Successfully uninstalled torch-1.7.0+cu101\n",
"Successfully installed torch-1.8.0.dev20201210+cu101 torchcsprng-0.2.0.dev20201210+cu101\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "su2RWWdOrWFU"
},
"source": [
"import torch\n",
"import torchcsprng as csprng"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "NHTOLPZ_3254"
},
"source": [
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "17L0sgmy0R6o"
},
"source": [
"torchcsprng implementation of AES with 128 bit key requires to have a key tensor of 16 bytes but of any dtype"
]
},
{
"cell_type": "code",
"metadata": {
"id": "rw7WYZ-50To9",
"outputId": "7e9accec-5ac1-4de2-fcc5-860df06ad338",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"key = torch.empty(16, dtype=torch.uint8, device=device).random_(0, 256)\n",
"key"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"tensor([ 94, 155, 111, 73, 36, 65, 95, 175, 103, 249, 39, 221, 225, 106,\n",
" 147, 18], dtype=torch.uint8)"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RRfvyfHM4MY1"
},
"source": [
"Alternatively it can be a tensor of 8 elements of `torch.int16` or even 4 elements of `torch.float32`"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rCy01t1-0dtO"
},
"source": [
"The size of input tensor is 42 * (32/8) = 168 bytes. AES 128 operates with 16-bytes blocks, so zero-padding of 8 bytes will be used to form 176 bytes(eleven 16-bytes blocks)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "LcuVmhyU0WTn",
"outputId": "2b887caa-ef4c-4e2a-bc7e-d00191cb727c",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"initial = torch.empty(42, dtype=torch.float32, device=device).normal_(-24.0, 42.0)\n",
"initial"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"tensor([ -33.2154, -4.4617, -2.9105, -37.1893, -6.0623, -47.4273,\n",
" -44.7682, 23.7199, 52.3595, -80.1355, 10.7041, -52.0069,\n",
" -59.1335, -51.7051, -96.7531, -47.6263, -37.4668, -26.9140,\n",
" -8.7239, -68.0884, -73.8865, -35.0378, -24.9022, -71.3780,\n",
" -83.3700, -73.9181, -56.9136, -14.0332, -0.1193, -24.6635,\n",
" -12.3755, 26.1501, -21.7292, 61.0362, -8.2424, -27.3287,\n",
" -116.2249, -32.5120, -56.9908, -44.0908, 15.9910, -12.2838])"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rPNq2u4e3tlJ"
},
"source": [
"torchcsprng requires output tensor to be of the same size in bytes as input tensor rounded up to 16 bytes(AES 128 block size), so if `torch.int64` is dtype of the destination tensor size must be 176 / (64/8) = 22"
]
},
{
"cell_type": "code",
"metadata": {
"id": "RAJya9GT0gb4"
},
"source": [
"encrypted = torch.empty(22, dtype=torch.int64, device=device)"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "-DCI4QOh4oGX"
},
"source": [
"Call `torchcsprng.encrypt` to encrypt `initial` tensor in [ECB](https://en.wikipedia.org/wiki/Block_cipher_mode_of_operation#Electronic_codebook_(ECB)) mode with 128-bit `key` tensor and store the result to `encrypted` tensor."
]
},
{
"cell_type": "code",
"metadata": {
"id": "TK4OjPRq4lsJ",
"outputId": "a9c7bee0-e95c-49fa-a6a8-8a33b58695e0",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"csprng.encrypt(initial, encrypted, key, \"aes128\", \"ecb\")"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"tensor([-1712310488599806515, -8311769289706173237, -8219472943074007291,\n",
" 4944862344658876406, 5289614356287313567, -6556419268017486570,\n",
" -2070634943111059129, -6988005632242749066, -6786984565899259673,\n",
" 2617853683610034142, 7394987086142621793, 9037048344238914391,\n",
" -4389724417591829293, 639143755047053321, 6630155308790632093,\n",
" 8233678956273288167, 7762736707078557167, -66820074992426839,\n",
" 2506358826618882964, 3811127519115226076, 7223286519671228245,\n",
" -4680879586720463643])"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yXUAwFHh5PSy"
},
"source": [
"Create an output tensor"
]
},
{
"cell_type": "code",
"metadata": {
"id": "4LtJ-kD446DJ"
},
"source": [
"decrypted = torch.empty_like(initial)"
],
"execution_count": 8,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "8VcF04mf6Rn5"
},
"source": [
"Call `torchcsprng.decrypt` to decrypt `encrypted` tensor in ECB mode with 128-bit `key` tensor and store the result to `decrypted` tensor."
]
},
{
"cell_type": "code",
"metadata": {
"id": "kojXCFGK5v6l"
},
"source": [
"# csprng.decrypt(encrypted, decrypted, key, \"aes128\", \"ecb\")"
],
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "yOc1ftnM5yyj"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}