h0ssn/xsum-calibration-700-mia

XSUM Calibration Set for Membership Inference Attacks

This dataset contains a calibration set for training membership inference attack (MIA) classifiers.

Dataset Information

• Total Samples: 700
• Members: 350 (sampled from training data)
• Non-members: 350 (sampled from unseen test split)
• Balance: 50.0% members / 50.0% non-members

Data Sources

Members (label=1)

• Source: 10K training data (excluding eval set)
• Dataset: mia-llm/xsum-raw-MIA (10K training samples)
• Filtering: Excluded samples that appear in evaluation set

Non-members (label=0)

• Source: Unseen test split
• Original Dataset:
  • AG News: fancyzhx/ag_news (test split)
  • XSum: EdinburghNLP/xsum (test split)
  • WikiText: Salesforce/wikitext wikitext-2-raw-v1 (test split)

Usage

Load Dataset

from datasets import load_dataset

# Load calibration set
dataset = load_dataset("h0ssn/xsum-calibration-700-mia")

# Access data
calibration_data = dataset['train']

print(f"Total samples: {len(calibration_data)}")
print(f"First sample: {calibration_data[0]}")

Train MIA Classifier

from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
import numpy as np

# Load calibration set
calibration_data = load_dataset("h0ssn/xsum-calibration-700-mia")['train']

# Extract features (example: using loss and gradient)
# You need to compute these features using your target and reference models
features = extract_features(calibration_data['text'])  # Your feature extraction
labels = calibration_data['label']

# Train classifier
scaler = StandardScaler()
features_normalized = scaler.fit_transform(features)

classifier = LogisticRegression(max_iter=1000, random_state=42)
classifier.fit(features_normalized, labels)

# Now use this classifier to predict membership on unknown samples

Data Fields

• text (string): The text content
• label (int): 1 for members, 0 for non-members
• source (string): Origin of the sample ('10k_train' or 'unseen')

Verification

✅ No overlap between members and non-members: All samples have been verified to ensure no duplicates exist between the member and non-member sets.

Citation

If you use this calibration set in your research, please cite:

@dataset{xsum_calibration_mia,
  title = {XSUM Calibration Set for Membership Inference Attacks},
  author = {h0ssn},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/h0ssn/xsum-calibration-700-mia}
}

Related Datasets

• Evaluation Set: mia-llm/xsum_benchmark_roya (700 samples)
• Training Set: mia-llm/xsum-raw-MIA (10K samples)

License

MIT License - Free to use for research and commercial purposes.

Notes

• This calibration set is designed to be used SEPARATELY from the evaluation set
• No samples from this calibration set appear in the evaluation set
• Recommended split: Use this for training MIA classifiers, use evaluation set for testing
• Random seed: 42 (for reproducibility)

Contact

For questions or issues, please open an issue on the dataset repository.