aboutsummaryrefslogtreecommitdiff
path: root/analysis/scalarmults/simulate.ipynb
diff options
context:
space:
mode:
Diffstat (limited to 'analysis/scalarmults/simulate.ipynb')
-rw-r--r--analysis/scalarmults/simulate.ipynb268
1 files changed, 101 insertions, 167 deletions
diff --git a/analysis/scalarmults/simulate.ipynb b/analysis/scalarmults/simulate.ipynb
index c1f123a..07f0e8f 100644
--- a/analysis/scalarmults/simulate.ipynb
+++ b/analysis/scalarmults/simulate.ipynb
@@ -25,12 +25,11 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"id": "b4386513-cc14-434b-a748-2863f8657452",
"metadata": {},
"outputs": [],
"source": [
- "import itertools\n",
"import gc\n",
"import glob\n",
"import hashlib\n",
@@ -52,7 +51,8 @@
"\n",
"from collections import Counter\n",
"from pathlib import Path\n",
- "from random import randint, randbytes\n",
+ "from random import randint, randbytes, randrange\n",
+ "from functools import partial\n",
"from typing import Type, Any\n",
"\n",
"from tqdm.auto import tqdm, trange\n",
@@ -64,7 +64,7 @@
"from pyecsca.sca.re.epa import graph_to_check_inputs, evaluate_checks\n",
"from pyecsca.misc.utils import TaskExecutor\n",
"\n",
- "from common import *"
+ "from epare import *"
]
},
{
@@ -80,7 +80,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 16,
"id": "3463a7bd-34d8-458b-8ceb-dddf99de21dc",
"metadata": {},
"outputs": [],
@@ -97,10 +97,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"id": "170c11fc-86cf-4eb1-bf4e-b2e44b2d7ac5",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Scalar multipliers considered: 65\n",
+ "Scalar multipliers (with a combination of up-to two countermeasures) considered: 5265\n",
+ "Error models considered: 32\n",
+ "Total configurations considered: 168480\n"
+ ]
+ }
+ ],
"source": [
"nmults = len(all_mults)\n",
"nmults_ctr = len(all_mults_with_ctr)\n",
@@ -108,7 +119,7 @@
"ncfgs = nmults_ctr * nerror_models\n",
"\n",
"print(f\"Scalar multipliers considered: {nmults}\")\n",
- "print(f\"Scalar multipliers (with a single countermeasure) considered: {nmults_ctr}\")\n",
+ "print(f\"Scalar multipliers (with a combination of up-to two countermeasures) considered: {nmults_ctr}\")\n",
"print(f\"Error models considered: {nerror_models}\")\n",
"print(f\"Total configurations considered: {ncfgs}\")"
]
@@ -126,7 +137,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
"id": "4d5c7f10-618f-4612-b594-81d1607b0d1d",
"metadata": {},
"outputs": [],
@@ -139,170 +150,19 @@
"use_init = True\n",
"use_multiply = True\n",
"\n",
- "num_workers = 30\n",
+ "num_workers = 20\n",
"samples = 1000\n",
"\n",
"selected_mults = all_mults"
]
},
{
- "cell_type": "code",
- "execution_count": null,
- "id": "07bc266d-35eb-4f6d-bdba-e9f6f66827f1",
- "metadata": {},
- "outputs": [],
- "source": [
- "def simulate_multiples(mult: MultIdent,\n",
- " params: DomainParameters,\n",
- " bits: int,\n",
- " samples: int = 100,\n",
- " seed: bytes | None = None) -> MultResults:\n",
- " \"\"\"\n",
- " Takes a MultIdent, which specifies a scalar multiplier (with an optional countermeasure)\n",
- " and simulates `samples` scalar multiplications, while tracking which multiples of the\n",
- " symbolic input point get computed.\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",
- " results.append(multiple_graph(scalar, params, mult.klass, mult.partial))\n",
- " return MultResults(results, samples)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6f3e9ffa-19e8-46b2-a6ad-1d318d5c8e17",
- "metadata": {},
- "outputs": [],
- "source": [
- "def simulate_multiples_direct(mult: MultIdent,\n",
- " params: DomainParameters,\n",
- " bits: int,\n",
- " fname: str,\n",
- " samples: int = 100,\n",
- " seed: bytes | None = None) -> str:\n",
- " \"\"\"\n",
- " Like the `simulate_multiples` function above, but stores the pickled output directly\n",
- " into a file named `fname`.\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",
- " results.append(multiple_graph(scalar, params, mult.klass, mult.partial))\n",
- " result = MultResults(results, samples)\n",
- " with open(fname, \"wb\") as f:\n",
- " pickle.dump((mult, result), f)\n",
- " return fname"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "64799c16-8113-4eff-81de-6a3e547eb5c5",
- "metadata": {},
- "outputs": [],
- "source": [
- "def evaluate_multiples(mult: MultIdent,\n",
- " res: MultResults,\n",
- " divisors: set[int],\n",
- " use_init: bool = True,\n",
- " use_multiply: bool = True):\n",
- " \"\"\"\n",
- " Takes MultIdent and MultResults and a set of divisors (base point orders `q`) and\n",
- " evaluates them using the error model from the MultIdent. Note that the MultIdent\n",
- " must have an error model in this case. Returns the ProbMap.\n",
- " \"\"\"\n",
- " errors = {divisor: 0 for divisor in divisors}\n",
- " samples = len(res)\n",
- " divisors_hash = hashlib.blake2b(str(sorted(divisors)).encode(), digest_size=8).digest()\n",
- " for precomp_ctx, full_ctx, out in res:\n",
- " check_inputs = graph_to_check_inputs(precomp_ctx, full_ctx, out,\n",
- " check_condition=mult.error_model.check_condition,\n",
- " precomp_to_affine=mult.error_model.precomp_to_affine,\n",
- " use_init=use_init,\n",
- " use_multiply=use_multiply)\n",
- " for q in divisors:\n",
- " error = evaluate_checks(check_funcs={\"add\": mult.error_model.check_add(q), \"affine\": mult.error_model.check_affine(q)},\n",
- " check_inputs=check_inputs)\n",
- " errors[q] += error\n",
- " # Make probmaps smaller. Do not store zero probabilities.\n",
- " probs = {}\n",
- " for q, error in errors.items():\n",
- " if error != 0:\n",
- " probs[q] = error / samples\n",
- " return ProbMap(probs, divisors_hash, samples)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ac630a08-4120-41cf-b3bb-1827ef469542",
- "metadata": {},
- "outputs": [],
- "source": [
- "def evaluate_multiples_direct(mult: MultIdent,\n",
- " fname: str,\n",
- " offset: int,\n",
- " divisors: set[int],\n",
- " use_init: bool = True,\n",
- " use_multiply: bool = True):\n",
- " \"\"\"\n",
- " Like `evaluate_multiples`, but instead reads the MultResults from a file named `fname`\n",
- " at an `offset`. Still returns the ProbMap, which is significantly smaller and easier\n",
- " to pickle than the MultResults.\n",
- " \"\"\"\n",
- " with open(fname, \"rb\") as f:\n",
- " f.seek(offset)\n",
- " _, res = pickle.load(f)\n",
- " errors = {divisor: 0 for divisor in divisors}\n",
- " samples = len(res)\n",
- " divisors_hash = hashlib.blake2b(str(sorted(divisors)).encode(), digest_size=8).digest()\n",
- " for precomp_ctx, full_ctx, out in res:\n",
- " check_inputs = graph_to_check_inputs(precomp_ctx, full_ctx, out,\n",
- " check_condition=mult.error_model.check_condition,\n",
- " precomp_to_affine=mult.error_model.precomp_to_affine,\n",
- " use_init=use_init,\n",
- " use_multiply=use_multiply)\n",
- " for q in divisors:\n",
- " error = evaluate_checks(check_funcs={\"add\": mult.error_model.check_add(q), \"affine\": mult.error_model.check_affine(q)},\n",
- " check_inputs=check_inputs)\n",
- " errors[q] += error\n",
- " # Make probmaps smaller. Do not store zero probabilities.\n",
- " probs = {}\n",
- " for q, error in errors.items():\n",
- " if error != 0:\n",
- " probs[q] = error / samples\n",
- " return ProbMap(probs, divisors_hash, samples)"
- ]
- },
- {
"cell_type": "markdown",
"id": "3aaf712e-5b97-4390-8dd4-e1db1dfe36a2",
"metadata": {},
"source": [
"## Run\n",
- "Run this cell as many times as you want. It will simulate `samples` scalar multiplications for each `MultIdent` (a scalar multiplier implementation with an optional countermeasure) and store them into the chunk."
+ "Run this cell as many times as you want. It will simulate `samples` scalar multiplications for each `Config` (a scalar multiplier implementation with an optional countermeasure) and store them into the chunk."
]
},
{
@@ -310,12 +170,35 @@
"execution_count": null,
"id": "84359084-4116-436c-92cd-d43fdfeca842",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "f7b8ac781be9408db32f219b4b2290a0",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Computing multiple graphs.: 0%| | 0/5265 [00:00<?, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/xjancar/pyecsca/virt/lib/python3.12/site-packages/loky/process_executor.py:782: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ],
"source": [
"chunk_id = randbytes(4).hex()\n",
"with TaskExecutor(max_workers=num_workers, initializer=silence) as pool, tempfile.TemporaryDirectory() as tmp_dirname:\n",
" tmp_path = Path(tmp_dirname)\n",
- " for i, mult in enumerate(all_mults_with_ctr):\n",
+ " for i, mult in enumerate(all_configs):\n",
" pool.submit_task(mult,\n",
" simulate_multiples_direct,\n",
" mult, params, bits, tmp_path / f\"{i}.pickle\", samples, seed=chunk_id)\n",
@@ -324,7 +207,8 @@
" #print(f\"Got {mult}.\")\n",
" if error := future.exception():\n",
" print(\"Error!\", error)\n",
- " continue\n",
+ " break\n",
+ " #continue\n",
" fpath = future.result()\n",
" with fpath.open(\"rb\") as f:\n",
" h.write(f.read())\n",
@@ -351,7 +235,57 @@
"execution_count": null,
"id": "fbab8333-b8f1-4890-b38a-7bb34f5ffb02",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "f4bc80bd47b345e0a74ad1a94121bfb1",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Processing chunks: 0%| | 0/1 [00:00<?, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing chunk 316daffc, no probmaps found.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "38046ebec4d34694a973ab1959f14905",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Loading chunk 316daffc.: 0%| | 0/5265 [00:00<?, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "76e3fb85f95e41069c2b7fbe86b27650",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Processing 316daffc.: 0%| | 0/168480 [00:00<?, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"with TaskExecutor(max_workers=num_workers, initializer=silence) as pool:\n",
" for in_fname in tqdm(glob.glob(f\"multiples_{bits}_chunk*.pickle\"), desc=\"Processing chunks\", smoothing=0):\n",
@@ -364,7 +298,7 @@
" out_file = Path(out_fname)\n",
"\n",
" cfgs_todo = set()\n",
- " for mult in all_mults_with_ctr:\n",
+ " for mult in all_configs:\n",
" for error_model in all_error_models:\n",
" cfgs_todo.add(mult.with_error_model(error_model))\n",
"\n",
generated by cgit v1.2.3-70-g09d2 (git 2.51.2) at 2025-11-09 11:49:27 +0000