Presents a systematic framework for evaluating MIAs across the full ML pipeline with standardized threat models and complementary metrics for different cost scenarios.
Practical blind membership inference attack via differential comparisons.arXiv preprint arXiv:2101.01341, 2021
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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.