yuvalgolbari/ImpMIA-data

ImpMIA Data

Data for ImpMIA: Leveraging Implicit Bias for Membership Inference Attack.

Layout

data/
  no_auxiliary_knowledge/{cifar10,cifar100,cinic10}/
  full_auxiliary_knowledge/cinic10/

Each directory contains x_train.npy, y_train.npy, x_test.npy, and y_test.npy. Images are uint8 arrays in NHWC format; labels are int64.

The No-Auxiliary-Knowledge candidate pools combine 30,000 target-distribution images with auxiliary images from CINIC-10, Open Images, or generated data. Their first 30,000 rows form the target-training source pool. The Full-Auxiliary-Knowledge CINIC-10 pool contains 50,000 CINIC-10 images.

Licensing

See LICENSES.md and the Open Images attribution tables under metadata/.

Citation

@article{golbari2026impmia,
  title   = {ImpMIA: Leveraging Implicit Bias for Membership Inference Attack},
  author  = {Golbari, Yuval and Wasserman, Navve and Vardi, Gal and Irani, Michal},
  journal = {Transactions on Machine Learning Research},
  year    = {2026}
}