Datasets:

DLSCA
/

ascad_v1_fk

Tags:

Dataset Viewer

The dataset viewer is not available for this subset.

Cannot get the split names for the config 'default' of the dataset.

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 91, in _split_generators
                  pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 193, in _generate_tables
                  examples = [ujson_loads(line) for line in batch.splitlines()]
                              ^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/json.py", line 20, in ujson_loads
                  return pd.io.json.ujson_loads(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                         ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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ASCAD v1 Fixed Key Dataset

Dataset Summary

This repository contains the ASCAD v1 (Fixed Key) dataset, officially known as the ANSSI Side-Channel Analysis Database. It contains raw electromagnetic (EM) side-channel traces acquired from an 8-bit ATMega8515 microcontroller running a masked AES-128 software implementation. It allows for streaming directly into deep learning frameworks without needing to download the entire dataset locally.

Dataset Generation

Pipeline Description:

This script downloads, extracts, and uploads the optimized ASCAD v1 Fixed Key dataset to Hugging Face Hub. Contains fixed key traces and cryptodata for side-channel analysis.

Chunking Parameters Used: The arrays were constructed using the following chunk sizes. Knowing this is helpful for optimizing your dataloader read speeds:

CHUNK_SIZE_Y = 30000
CHUNK_SIZE_X = 20
TOTAL_CHUNKS_Y = 2
TOTAL_CHUNKS_X = 5000

Pipeline Dependencies: If you need to reproduce the extraction environment, the following packages were used: zarr, huggingface-hub, hf-transfer, fsspec, hffs, rich, numpy, h5py, certifi

Dataset Structure

The data is structured into a root Zarr group containing the raw traces and a cryptodata sub-group containing the corresponding cryptographic variables.

/traces: int8 array containing the raw side-channel measurements.
/cryptodata/plaintext: Plaintext byte arrays.
/cryptodata/ciphertext: Ciphertext byte arrays.
/cryptodata/key: The fixed encryption key byte arrays.
/cryptodata/mask: Masking material used during the AES execution.
/cryptodata/rin: The r_in mask arrays.
/cryptodata/rout: The r_out mask arrays.

How to Use

You can stream or load this dataset directly from the Hugging Face Hub using zarr and fsspec.

Python Example

Ensure you have the required dependencies installed:

pip install zarr fsspec huggingface-hub

import zarr
import fsspec

# Replace with your actual Hugging Face Org and Repo ID
repo_id = "DLSCA/your-repo-name"

# Map the Hugging Face repository via fsspec
mapper = fsspec.get_mapper(f"hf://datasets/{repo_id}")

# Open the Zarr root group in read-only mode
root = zarr.open_group(mapper, mode="r")

# Access traces and cryptodata (loaded lazily)
traces = root["traces"]
plaintext = root["cryptodata"]["plaintext"]
key = root["cryptodata"]["key"]

print(f"Traces shape: {traces.shape}")
print(f"Sample trace: {traces[0, :10]}")

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