You're currently viewing an old version of this dataset. To see the current version, click here.

Membership-Invariant Subspace Training

Membership-Invariant Subspace Training (MIST) is a method for training classifiers that acts as a defense designed to specifically defend against black-box membership inference attacks on the most vulnerable instances in the training data.

Data and Resources

This dataset has no data

Cite this as

Jiacheng Li, Ninghui Li, Bruno Ribeiro (2025). Dataset: Membership-Invariant Subspace Training. https://doi.org/10.57702/4f2ietq5

Private DOI This DOI is not yet resolvable.
It is available for use in manuscripts, and will be published when the Dataset is made public.

Additional Info

Field	Value
Created	January 3, 2025
Last update	January 3, 2025
Defined In	https://doi.org/10.48550/arXiv.2311.00919
Author	Jiacheng Li
More Authors	Ninghui Li Bruno Ribeiro