dataset_card.md

---
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