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---
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
```python
# 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
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