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Diffstat (limited to 'analysis/countermeasures/simulate.ipynb')
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diff --git a/analysis/countermeasures/simulate.ipynb b/analysis/countermeasures/simulate.ipynb new file mode 100644 index 0000000..67ed0e4 --- /dev/null +++ b/analysis/countermeasures/simulate.ipynb @@ -0,0 +1,265 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "805d746e-610b-4d40-80d2-a8080a993f96", + "metadata": {}, + "source": [ + "# Simulating EPA-RE using points of low-order" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b4386513-cc14-434b-a748-2863f8657452", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "import itertools\n", + "import glob\n", + "\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from collections import Counter\n", + "\n", + "from pathlib import Path\n", + "from random import randint, randbytes\n", + "from typing import Type, Any\n", + "\n", + "from bs4 import BeautifulSoup\n", + "from tqdm.auto import tqdm, trange\n", + "\n", + "from pyecsca.ec.params import DomainParameters, get_params\n", + "from pyecsca.ec.mult import *\n", + "from pyecsca.sca.re.rpa import multiples_computed\n", + "from pyecsca.misc.utils import TaskExecutor\n", + "\n", + "from common import *" + ] + }, + { + "cell_type": "markdown", + "id": "5b156d2a-7345-47f8-a76e-71a7d2be9d22", + "metadata": {}, + "source": [ + "## Initialize" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a660e3ac-401b-47a0-92de-55afe63c420a", + "metadata": {}, + "outputs": [], + "source": [ + "print(len(all_mults))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a95b27fc-96a9-41b5-9972-dc8386ed386d", + "metadata": {}, + "outputs": [], + "source": [ + "print(len(all_mults_with_ctr))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "07bc266d-35eb-4f6d-bdba-e9f6f66827f1", + "metadata": {}, + "outputs": [], + "source": [ + "# Needs imports on the inside to be spawn enabled to save memory.\n", + "\n", + "def get_general_multiples(bits: int, samples: int = 1000) -> MultResults:\n", + " from random import randint\n", + " results = []\n", + " for _ in range(samples):\n", + " big_scalar = randint(1, 2**bits)\n", + " results.append({big_scalar})\n", + " return MultResults(results, samples)\n", + "\n", + "def get_general_n_multiples(bits: int, n: int, samples: int = 1000) -> MultResults:\n", + " from random import randint\n", + " results = []\n", + " for _ in range(samples):\n", + " smult = set()\n", + " for i in range(n):\n", + " b = randint(1,256)\n", + " smult.add(randint(2**b,2**(b+1)))\n", + " results.append(smult)\n", + " return MultResults(results, samples)\n", + "\n", + "def get_small_scalar_multiples(mult: MultIdent,\n", + " params: DomainParameters,\n", + " bits: int,\n", + " samples: int = 100,\n", + " use_init: bool = True,\n", + " use_multiply: bool = True,\n", + " seed: bytes | None = None,\n", + " kind: str = \"precomp+necessary\") -> MultResults:\n", + " from pyecsca.sca.re.rpa import multiples_computed\n", + " import random\n", + " \n", + " results = []\n", + " if seed is not None:\n", + " random.seed(seed)\n", + "\n", + " # If no countermeasure is used, we have fully random scalars.\n", + " # Otherwise, fix one per chunk.\n", + " if mult.countermeasure is None:\n", + " scalars = [random.randint(1, 2**bits) for _ in range(samples)]\n", + " else:\n", + " one = random.randint(1, 2**bits)\n", + " scalars = [one for _ in range(samples)]\n", + "\n", + " for scalar in scalars:\n", + " # Use a list for less memory usage.\n", + " results.append(list(multiples_computed(scalar, params, mult.klass, mult.partial, use_init, use_multiply, kind=kind)))\n", + " return MultResults(results, samples)" + ] + }, + { + "cell_type": "markdown", + "id": "8c5e9543-8447-4362-b9e2-c896d71f69a9", + "metadata": {}, + "source": [ + "## Prepare" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d5c7f10-618f-4612-b594-81d1607b0d1d", + "metadata": {}, + "outputs": [], + "source": [ + "category = \"secg\"\n", + "curve = \"secp256r1\"\n", + "kind = \"precomp+necessary\"\n", + "use_init = True\n", + "use_multiply = True\n", + "params = get_params(category, curve, \"projective\")\n", + "num_workers = 20\n", + "bits = params.order.bit_length()\n", + "samples = 100\n", + "selected_mults = all_mults" + ] + }, + { + "cell_type": "markdown", + "id": "3aaf712e-5b97-4390-8dd4-e1db1dfe36a2", + "metadata": {}, + "source": [ + "## Run\n", + "Run this cell as many times as you want. It will write chunks into files." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84359084-4116-436c-92cd-d43fdfeca842", + "metadata": {}, + "outputs": [], + "source": [ + "multiples_mults = {}\n", + "chunk_id = randbytes(4).hex()\n", + "with TaskExecutor(max_workers=num_workers, mp_context=spawn_context) as pool, enable_spawn(get_small_scalar_multiples) as target:\n", + " for mult in selected_mults:\n", + " for countermeasure in (None, \"gsr\", \"additive\", \"multiplicative\", \"euclidean\", \"bt\"):\n", + " mwc = mult.with_countermeasure(countermeasure)\n", + " pool.submit_task(mwc,\n", + " target,\n", + " mwc, params, bits, samples, seed=chunk_id, kind=kind, use_init=use_init, use_multiply=use_multiply)\n", + " for mult, future in tqdm(pool.as_completed(), desc=\"Computing small scalar distributions.\", total=len(pool.tasks)):\n", + " print(f\"Got {mult}.\")\n", + " if error := future.exception():\n", + " print(\"Error!\", error)\n", + " continue\n", + " res = future.result()\n", + " if mult not in multiples_mults:\n", + " multiples_mults[mult] = res\n", + " else:\n", + " # Accumulate\n", + " multiples_mults[mult].merge(res)\n", + "\n", + " # Handle the enable_spawn trick that messes up class modules.\n", + " for k, v in multiples_mults.items():\n", + " v.__class__ = MultResults\n", + " v.__module__ = \"common\"\n", + "\n", + "# Save\n", + "with open(f\"multiples_{bits}_{'init' if use_init else 'noinit'}_{'mult' if use_multiply else 'nomult'}_chunk{chunk_id}.pickle\",\"wb\") as h:\n", + " for mult, res in multiples_mults.items():\n", + " pickle.dump((mult, res), h)" + ] + }, + { + "cell_type": "markdown", + "id": "b4471a1d-fdc3-4be7-bd61-5ddd22180b41", + "metadata": {}, + "source": [ + "### Load\n", + "**Beware**, the following load with try to load all chunks into memory, that will be very large.\n", + "\n", + "You probably dont want to run this." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3d291832-b0c7-4c3a-9989-22079e4e0f53", + "metadata": {}, + "outputs": [], + "source": [ + "multiples_mults = {}\n", + "for fname in glob.glob(f\"multiples_{bits}_{'init' if use_init else 'noinit'}_{'mult' if use_multiply else 'nomult'}_chunk*.pickle\"):\n", + " with open(fname, \"rb\") as f:\n", + " while True:\n", + " try:\n", + " mult, vals = pickle.load(f)\n", + " if mult not in multiples_mults:\n", + " multiples_mults[mult] = vals\n", + " else:\n", + " multiples_mults[mult].merge(vals)\n", + " except EOFError:\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11b447f2-71ab-417e-a856-1724788cfc91", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} |