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+{
+ "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": 4,
+ "id": "b4386513-cc14-434b-a748-2863f8657452",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pickle\n",
+ "import itertools\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\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 MultipleContext, rpa_distinguish, RPA, multiples_computed\n",
+ "from pyecsca.ec.context import DefaultContext, local\n",
+ "from pyecsca.ec.model import ShortWeierstrassModel\n",
+ "from pyecsca.ec.coordinates import AffineCoordinateModel\n",
+ "from pyecsca.misc.utils import TaskExecutor\n",
+ "\n",
+ "from common import MultIdent, MultResults, enable_spawn, spawn_context"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5b156d2a-7345-47f8-a76e-71a7d2be9d22",
+ "metadata": {},
+ "source": [
+ "## Initialize"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "5c0e42dc-8c61-4e2e-962c-6af48f6eb321",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# All dbl-and-add multipliers from https://github.com/J08nY/pyecsca/blob/master/pyecsca/ec/mult\n",
+ "\n",
+ "window_mults = [\n",
+ " MultIdent(SlidingWindowMultiplier, width=3),\n",
+ " MultIdent(SlidingWindowMultiplier, width=4),\n",
+ " MultIdent(SlidingWindowMultiplier, width=5),\n",
+ " MultIdent(SlidingWindowMultiplier, width=6),\n",
+ " MultIdent(FixedWindowLTRMultiplier, m=2**4),\n",
+ " MultIdent(FixedWindowLTRMultiplier, m=2**5),\n",
+ " MultIdent(FixedWindowLTRMultiplier, m=2**6),\n",
+ " MultIdent(WindowBoothMultiplier, width=3),\n",
+ " MultIdent(WindowBoothMultiplier, width=4),\n",
+ " MultIdent(WindowBoothMultiplier, width=5),\n",
+ " MultIdent(WindowBoothMultiplier, width=6)\n",
+ "]\n",
+ "naf_mults = [\n",
+ " MultIdent(WindowNAFMultiplier, width=3),\n",
+ " MultIdent(WindowNAFMultiplier, width=4),\n",
+ " MultIdent(WindowNAFMultiplier, width=5),\n",
+ " MultIdent(WindowNAFMultiplier, width=6),\n",
+ " MultIdent(BinaryNAFMultiplier, direction=ProcessingDirection.LTR),\n",
+ " MultIdent(BinaryNAFMultiplier, direction=ProcessingDirection.RTL)\n",
+ "]\n",
+ "comb_mults = [\n",
+ " MultIdent(CombMultiplier, width=2),\n",
+ " MultIdent(CombMultiplier, width=3),\n",
+ " MultIdent(CombMultiplier, width=4),\n",
+ " MultIdent(CombMultiplier, width=5),\n",
+ " MultIdent(CombMultiplier, width=6),\n",
+ " MultIdent(BGMWMultiplier, width=2, direction=ProcessingDirection.LTR),\n",
+ " MultIdent(BGMWMultiplier, width=3, direction=ProcessingDirection.LTR),\n",
+ " MultIdent(BGMWMultiplier, width=4, direction=ProcessingDirection.LTR),\n",
+ " MultIdent(BGMWMultiplier, width=5, direction=ProcessingDirection.LTR),\n",
+ " MultIdent(BGMWMultiplier, width=6, direction=ProcessingDirection.LTR),\n",
+ " MultIdent(BGMWMultiplier, width=2, direction=ProcessingDirection.RTL),\n",
+ " MultIdent(BGMWMultiplier, width=3, direction=ProcessingDirection.RTL),\n",
+ " MultIdent(BGMWMultiplier, width=4, direction=ProcessingDirection.RTL),\n",
+ " MultIdent(BGMWMultiplier, width=5, direction=ProcessingDirection.RTL),\n",
+ " MultIdent(BGMWMultiplier, width=6, direction=ProcessingDirection.RTL)\n",
+ "]\n",
+ "binary_mults = [\n",
+ " MultIdent(LTRMultiplier, always=False),\n",
+ " MultIdent(LTRMultiplier, always=True),\n",
+ " MultIdent(RTLMultiplier, always=False),\n",
+ " MultIdent(RTLMultiplier, always=True),\n",
+ " MultIdent(CoronMultiplier)\n",
+ "]\n",
+ "other_mults = [\n",
+ " MultIdent(FullPrecompMultiplier, always=False),\n",
+ " MultIdent(FullPrecompMultiplier, always=True),\n",
+ " MultIdent(SimpleLadderMultiplier, complete=True),\n",
+ " MultIdent(SimpleLadderMultiplier, complete=False)\n",
+ "]\n",
+ "\n",
+ "all_mults = window_mults + naf_mults + binary_mults + other_mults + comb_mults"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "a660e3ac-401b-47a0-92de-55afe63c420a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "41\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(len(all_mults))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "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, params: DomainParameters, bits: int, samples: int = 1000, use_init: bool = True, use_multiply: bool = True) -> MultResults:\n",
+ " from pyecsca.sca.re.rpa import multiples_computed\n",
+ " from random import randint\n",
+ " results = []\n",
+ " for _ in range(samples):\n",
+ " big_scalar = randint(1, 2**bits)\n",
+ " results.append(multiples_computed(big_scalar, params, mult.klass, mult.partial, use_init, use_multiply))\n",
+ " return MultResults(results, samples)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8c5e9543-8447-4362-b9e2-c896d71f69a9",
+ "metadata": {},
+ "source": [
+ "## Prepare"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "bb604b15-4ad6-43c0-9cfa-1b31611d73ce",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "multiples_mults = {}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "4d5c7f10-618f-4612-b594-81d1607b0d1d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "category = \"secg\"\n",
+ "curve = \"secp256r1\"\n",
+ "params = get_params(category, curve, \"projective\")\n",
+ "num_workers = 20\n",
+ "bits = params.order.bit_length()\n",
+ "samples = 1000\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 accumulate into multiples_mults."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "84359084-4116-436c-92cd-d43fdfeca842",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "8050887d56444467ae4a9e8345acaab5",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Computing small scalar distributions.: 0%| | 0/41 [00:00<?, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "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",
+ " pool.submit_task(mult,\n",
+ " target,\n",
+ " mult, params, bits, samples)\n",
+ " for mult, future in tqdm(pool.as_completed(), desc=\"Computing small scalar distributions.\", total=len(pool.tasks)):\n",
+ " print(f\"Got {mult_label(mult)}.\")\n",
+ " if error := future.exception():\n",
+ " print(error)\n",
+ " continue\n",
+ " if mult not in multiples_mults:\n",
+ " multiples_mults[mult] = res\n",
+ " else:\n",
+ " # Accumulate\n",
+ " multiples_mults[mult].merge(res)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6845ba69-74b0-4709-a64d-dd4860255ee2",
+ "metadata": {},
+ "source": [
+ "### Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0ae7f726-2981-48af-8ae3-a9afcf2dc18f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open(f\"multiples_{category}_{curve}_{bits}\",\"wb\") as h:\n",
+ " pickle.dump(multiples_mults, h)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b4471a1d-fdc3-4be7-bd61-5ddd22180b41",
+ "metadata": {},
+ "source": [
+ "### Load"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3d291832-b0c7-4c3a-9989-22079e4e0f53",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open(f\"multiples_{category}_{curve}_{bits}\", \"rb\") as f:\n",
+ " multiples_mults = pickle.load(f)"
+ ]
+ }
+ ],
+ "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
+}
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