WikiMIA_2024 Hard is a challenging dataset for membership inference attacks intorduced in the paper "The Surprising Effectiveness of Membership Inference with Simple N-Gram Coverage" containing temporal Wikipedia articles with different versions based on date cutoffs.
This dataset is designed to evaluate the robustness of privacy-preserving machine learning models against sophisticated membership inference techniques.
It… See the full description on the dataset page: https://huggingface.co/datasets/hallisky/wikiMIA-2024-hard.
