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LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings
Membership inference attacks (MIAs) threaten the privacy of machine learning models by revealing whether a data point was used during training. Existing MIAs often assume access to public datasets, shadow models, confidence scores or the training distribution, which makes them vulnerable to defenses
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