dataset_card.md

metadata

annotations_creators:
  - expert-generated
language_creators:
  - found
language:
  - en
license: mit
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - other
task_ids:
  - other
pretty_name: DLSCA Test Dataset
tags:
  - side-channel-analysis
  - deep-learning
  - security
  - zarr
  - streaming
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/*
dataset_info:
  features:
    - name: labels
      sequence: int32
    - name: traces
      sequence: int8
    - name: index
      dtype: int32
  splits:
    - name: train
      num_bytes: 20971128
      num_examples: 1000
  download_size: 20987256
  dataset_size: 20971128

DLSCA Test Dataset

This dataset provides power consumption traces and corresponding labels for Deep Learning-based Side Channel Analysis (DLSCA) research.

Dataset Summary

The DLSCA Test Dataset contains 1,000 power consumption traces with corresponding cryptographic key labels. This dataset is designed for training and evaluating deep learning models in side-channel analysis scenarios.

Supported Tasks

• Side Channel Analysis: Predict cryptographic keys from power consumption traces
• Deep Learning: Train neural networks for cryptographic analysis
• Streaming Data Processing: Demonstrate efficient handling of large trace datasets

Dataset Structure

Data Instances

Each example contains:

• traces: Power consumption measurements (20,971 time points, int8)
• labels: Cryptographic key bytes (4 values, int32)
• index: Sequential example identifier (int32)

Data Fields

• traces: Sequence of 20,971 power consumption measurements
• labels: Sequence of 4 cryptographic key bytes
• index: Integer index of the example

Data Splits

The dataset contains a single training split with 1,000 examples.

Dataset Creation

Curation Rationale

This dataset was created to demonstrate efficient streaming capabilities for large-scale side-channel analysis datasets using zarr format with chunking.

Source Data

The traces represent power consumption measurements during cryptographic operations, with labels corresponding to secret key bytes.

Annotations

Labels represent the actual cryptographic key bytes used during the operations that generated the corresponding power traces.

Considerations for Using the Data

Social Impact of Dataset

This dataset is intended for security research and educational purposes in the field of side-channel analysis.

Discussion of Biases

The dataset represents a controlled laboratory environment and may not reflect real-world deployment scenarios.

Other Known Limitations

• Limited to 1,000 examples for demonstration purposes
• Single cryptographic implementation
• Controlled measurement environment

Additional Information

Dataset Curators

Created for the DLSCA project demonstrating streaming capabilities.

Licensing Information

MIT License

Citation Information

@dataset{dlsca_test_2025,
  title={DLSCA Test Dataset with Streaming Support},
  author={DLSCA Team},
  year={2025},
  url={https://huggingface.co/datasets/DLSCA/test}
}

Contributions

This dataset demonstrates advanced streaming capabilities for large-scale side-channel analysis using zarr format and Hugging Face datasets integration.

Technical Implementation

Streaming Support

The dataset implements custom streaming using:

• Zarr v2 format: For efficient chunked storage
• Zip compression: To minimize file count
• Hugging Face caching: For optimal performance
• Custom DownloadManager: For zarr chunk handling

Usage Examples

# Load with streaming support
from datasets import load_dataset
dataset = load_dataset("DLSCA/test", streaming=True)

# Access examples efficiently
for example in dataset["train"]:
    traces = example["traces"]
    labels = example["labels"]
    # Process example...

Performance Characteristics

• Memory efficient: Only loads required chunks
• Scalable: Works with datasets larger than available RAM
• Fast access: Optimized chunk-based retrieval
• Compressed storage: Zip format reduces storage requirements