Datasets:

DLSCA
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ascad-v2-small-merged

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train

The full dataset viewer is not available (click to read why). Only showing a preview of the rows.

The dataset generation failed

Error code:   DatasetGenerationError
Exception:    ArrowNotImplementedError
Message:      Cannot write struct type 'attributes' with no child field to Parquet. Consider adding a dummy child field.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1821, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                                            ^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 781, in finalize
                  self.write_rows_on_file()
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
                  self._write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 771, in _write_table
                  self._build_writer(inferred_schema=pa_table.schema)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 812, in _build_writer
                  self.pa_writer = pq.ParquetWriter(
                                   ^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pyarrow/parquet/core.py", line 1070, in __init__
                  self.writer = _parquet.ParquetWriter(
                                ^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/_parquet.pyx", line 2363, in pyarrow._parquet.ParquetWriter.__cinit__
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'attributes' with no child field to Parquet. Consider adding a dummy child field.
              
              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/parquet_and_info.py", line 1343, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 907, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1832, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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shape list	data_type string	chunk_grid dict	chunk_key_encoding dict	fill_value int64	codecs list	attributes dict	zarr_format int64	node_type string	storage_transformers list
[ 100000, 16 ]	uint8	{ "name": "regular", "configuration": { "chunk_shape": [ 100000, 16 ] } }	{ "name": "default", "configuration": { "separator": "/" } }	0	[ { "name": "bytes", "configuration": null }, { "name": "zstd", "configuration": { "level": 0, "checksum": true } } ]	{}	3	array	[]
[ 100000, 16 ]	uint8	{ "name": "regular", "configuration": { "chunk_shape": [ 100000, 16 ] } }	{ "name": "default", "configuration": { "separator": "/" } }	0	[ { "name": "bytes", "configuration": null }, { "name": "zstd", "configuration": { "level": 0, "checksum": true } } ]	{}	3	array	[]
[ 100000, 16 ]	uint8	{ "name": "regular", "configuration": { "chunk_shape": [ 100000, 16 ] } }	{ "name": "default", "configuration": { "separator": "/" } }	0	[ { "name": "bytes", "configuration": null }, { "name": "zstd", "configuration": { "level": 0, "checksum": true } } ]	{}	3	array	[]
[ 100000, 16 ]	uint8	{ "name": "regular", "configuration": { "chunk_shape": [ 100000, 16 ] } }	{ "name": "default", "configuration": { "separator": "/" } }	0	[ { "name": "bytes", "configuration": null }, { "name": "zstd", "configuration": { "level": 0, "checksum": true } } ]	{}	3	array	[]
[ 100000, 16 ]	uint8	{ "name": "regular", "configuration": { "chunk_shape": [ 100000, 16 ] } }	{ "name": "default", "configuration": { "separator": "/" } }	0	[ { "name": "bytes", "configuration": null }, { "name": "zstd", "configuration": { "level": 0, "checksum": true } } ]	{}	3	array	[]
[ 100000, 1 ]	uint8	{ "name": "regular", "configuration": { "chunk_shape": [ 100000, 1 ] } }	{ "name": "default", "configuration": { "separator": "/" } }	0	[ { "name": "bytes", "configuration": null }, { "name": "zstd", "configuration": { "level": 0, "checksum": true } } ]	{}	3	array	[]
[ 100000, 1 ]	uint8	{ "name": "regular", "configuration": { "chunk_shape": [ 100000, 1 ] } }	{ "name": "default", "configuration": { "separator": "/" } }	0	[ { "name": "bytes", "configuration": null }, { "name": "zstd", "configuration": { "level": 0, "checksum": true } } ]	{}	3	array	[]
[ 100000, 1 ]	uint8	{ "name": "regular", "configuration": { "chunk_shape": [ 100000, 1 ] } }	{ "name": "default", "configuration": { "separator": "/" } }	0	[ { "name": "bytes", "configuration": null }, { "name": "zstd", "configuration": { "level": 0, "checksum": true } } ]	{}	3	array	[]
[ 100000, 1 ]	uint8	{ "name": "regular", "configuration": { "chunk_shape": [ 100000, 1 ] } }	{ "name": "default", "configuration": { "separator": "/" } }	0	[ { "name": "bytes", "configuration": null }, { "name": "zstd", "configuration": { "level": 0, "checksum": true } } ]	{}	3	array	[]
[ 100000, 1 ]	uint8	{ "name": "regular", "configuration": { "chunk_shape": [ 100000, 1 ] } }	{ "name": "default", "configuration": { "separator": "/" } }	0	[ { "name": "bytes", "configuration": null }, { "name": "zstd", "configuration": { "level": 0, "checksum": true } } ]	{}	3	array	[]
[ 100000, 1 ]	uint8	{ "name": "regular", "configuration": { "chunk_shape": [ 100000, 1 ] } }	{ "name": "default", "configuration": { "separator": "/" } }	0	[ { "name": "bytes", "configuration": null }, { "name": "zstd", "configuration": { "level": 0, "checksum": true } } ]	{}	3	array	[]
null	null	null	null	null	null	{}	3	group	null
[ 100000, 400000 ]	int8	{ "name": "regular", "configuration": { "chunk_shape": [ 50000, 200 ] } }	{ "name": "default", "configuration": { "separator": "/" } }	0	[ { "name": "bytes", "configuration": null }, { "name": "zstd", "configuration": { "level": 0, "checksum": true } } ]	{}	3	array	[]
null	null	null	null	null	null	{}	3	group	null

ascad-v2-small-merged

This script fetches slices from existing ASCAD v2 datasets on Hugging Face and merges them into a smaller, tightly-focused dataset (e.g., Round 1 target traces) based on user-defined bounds.

Dataset Structure

This dataset is stored in Zarr format, optimized for chunked and compressed cloud storage.

Traces (`/traces`)

Shape: [100000, 400000] (Traces x Time Samples)
Data Type: int8
Chunk Shape: [50000, 200]

Metadata (`/metadata`)

ciphertext: shape [100000, 16], dtype uint8
key: shape [100000, 16], dtype uint8
mask: shape [100000, 16], dtype uint8
mask_: shape [100000, 16], dtype uint8
plaintext: shape [100000, 16], dtype uint8
rin: shape [100000, 1], dtype uint8
rin_: shape [100000, 1], dtype uint8
rm: shape [100000, 1], dtype uint8
rm_: shape [100000, 1], dtype uint8
rout: shape [100000, 1], dtype uint8
rout_: shape [100000, 1], dtype uint8

Leakage Analysis Targets

The following targets are available for side-channel leakage analysis on this dataset:

Target Name	Description
`ciphertext`	Raw ciphertext byte at position `byte_index`. `ciphertext[i]` where `i = byte_index`.
`key`	Raw key byte at position `byte_index`. `key[i]` where `i = byte_index`.
`mask`	Raw per-byte mask at position `byte_index`. `mask[i]` where `i = byte_index`.
`mask_`	Raw per-byte mask for second S-box pass at position `byte_index`. `mask_[i]` where `i = byte_index`.
`perm_index`	Raw permutation index at shuffling slot `byte_index`. `perm_index[i]` where `i = byte_index`.
`plaintext`	Raw plaintext byte at position `byte_index`. `plaintext[i]` where `i = byte_index`.
`rin`	Raw global input mask `rin`.
`rin_`	Raw global input mask `rin_` for second S-box pass.
`rm`	Raw global multiplicative mask `rm` (alpha).
`rm_`	Raw global multiplicative mask `rm_` for second S-box pass.
`rout`	Raw global output mask `rout`.
`rout_`	Raw global output mask `rout_` for second S-box pass.
`sbox_masked`	Raw `sbox_masked` label at shuffling slot `byte_index`. `sbox_masked[i]` where `i = byte_index`.
`sbox_masked_with_perm`	Raw `sbox_masked_with_perm` label at AES byte position `byte_index`. `sbox_masked_with_perm[i]` where `i = byte_index`.
`v2_hd_ptx_sbi`	HD between the unmasked plaintext and the unmasked SBI. `HD(ptx[j], ptx[j] ^ key[j])` where `j = perm[byte_index]`.
`v2_hd_rout_mask_interaction`	Hamming distance between the global rout mask and the per-byte mask. `HD(rout, mask[j])` where `j = perm[byte_index]`.
`v2_hd_sbi_sbo`	HD between the unmasked SBI and unmasked SBO. `HD(ptx[j] ^ key[j], SBOX(ptx[j] ^ key[j]))` where `j = perm[byte_index]`.
`v2_hd_sbo_affine_mc`	HD between the affine SBO and the masked MixColumns output. `HD(rm*SBOX(ptx[j]^key[j])^mask[j], MixColumns(...)[j])` where `j = perm[byte_index]`.
`v2_hd_xw_lut_idx`	HD between the pre-LUT state (Xor_Word applied) and the LUT index. `HD(rm(ptx[j]^key[j])^mask[j]^rin, rm(ptx[j]^key[j])^rin)` where `j = perm[byte_index]`.
`v2_hw_affine_ptx`	HW of the affine-masked plaintext. `HW(rm * ptx[j] ^ mask[j])` where `j = perm[byte_index]`.
`v2_hw_key`	HW of the key byte at the permuted AES position. `HW(key[j])` where `j = perm[byte_index]`.
`v2_hw_lut_idx`	HW of the sboxMasked LUT index. `HW(rm * (ptx[j] ^ key[j]) ^ rin)` where `j = perm[byte_index]`.
`v2_hw_mask`	HW of the per-byte additive mask. `HW(mask[j])` where `j = perm[byte_index]`.
`v2_hw_masked_sbi`	HW of the affine-masked SBI entering round 1. `HW(rm * (ptx[j] ^ key[j]) ^ mask[j])` where `j = perm[byte_index]`.
`v2_hw_mixcolumns_masked`	HW of the affine-masked MixColumns output. `HW(MixColumns(ShiftRows(rm * SBOX(ptx ^ key) ^ mask))[j])` where `j = perm[byte_index]`.
`v2_hw_ptx`	HW of plaintext at the permuted AES position. `HW(ptx[j])` where `j = perm[byte_index]`.
`v2_hw_raw_out`	HW of the sboxMasked LUT output. `HW(rm * SBOX(ptx[j] ^ key[j]) ^ rout)` where `j = perm[byte_index]`.
`v2_hw_rm_key`	HW of the multiplicatively masked key byte. `HW(rm * key[j])` where `j = perm[byte_index]`.
`v2_hw_rm_ptx`	HW of the Map_in_G output (multiplicatively masked plaintext). `HW(rm * ptx[j])` where `j = perm[byte_index]`.
`v2_hw_sbi`	HW of the unmasked SBI at the permuted AES position. `HW(ptx[j] ^ key[j])` where `j = perm[byte_index]`.
`v2_hw_sbo`	HW of the unmasked SBO at the permuted AES position. `HW(SBOX(ptx[j] ^ key[j]))` where `j = perm[byte_index]`.
`v2_hw_sbo_affine`	HW of the post-SubBytes affine state. `HW(rm * SBOX(ptx[j] ^ key[j]) ^ mask[j])` where `j = perm[byte_index]`.
`v2_hw_sbo_mid`	HW of the mid-SubBytes state (rout and per-byte mask applied). `HW(rm * SBOX(ptx[j] ^ key[j]) ^ rout ^ mask[j])` where `j = perm[byte_index]`.
`v2_id_masked_sbi`	256-class Identity target for the affine-masked SBI. Passes the exact byte value directly to the DL model.
`v2_id_sbo_affine`	256-class Identity target for the affine-masked SBO (post-rout strip). Passes the exact byte value directly to the DL model.

Parameters Used for Generation

HF_ORG: DLSCA
CHUNK_SIZE_Y: 50000
CHUNK_SIZE_X: 200
TOTAL_CHUNKS_ON_Y: 2
TOTAL_CHUNKS_ON_X: 2000
NUM_JOBS: 1
COMPRESSED: True
CAN_RUN_LOCALLY: True
CAN_RUN_ON_CLOUD: True

Usage

You can load this dataset directly using Zarr and Hugging Face File System:

import zarr
from huggingface_hub import HfFileSystem

fs = HfFileSystem()

# Map only once to the dataset root
root = zarr.open_group(fs.get_mapper("datasets/DLSCA/ascad-v2-small-merged"), mode="r")

# Access traces directly
traces = root["traces"]
print("Traces shape:", traces.shape)

# Access plaintext metadata directly
plaintext = root["metadata"]["plaintext"]
print("Plaintext shape:", plaintext.shape)

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