Presents a systematic framework for evaluating MIAs across the full ML pipeline with standardized threat models and complementary metrics for different cost scenarios.
Revisiting membership inference under realistic assumptions.arXiv preprint arXiv:2005.10881, 2020
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A Full-Pipeline Framework for Evaluating Membership Inference Attacks in Machine Learning
Presents a systematic framework for evaluating MIAs across the full ML pipeline with standardized threat models and complementary metrics for different cost scenarios.