Calibrating One-Round Membership Inference with Neighbors
The state-of-the-art Membership Inference (MI) methods calibrate their signal separately for each example using reference models, auxiliary models trained to exclude the target. This paradigm scales poorly to modern large models, however, whose training is too expensive to replicate. This has motivated one-round settings, where only a single trained model is available; but without reference models the per-example calibration that drives the strongest attacks can no longer be estimated, leaving the membership signal weak. We ask whether neighbors of the target point can recover this calibration without training any additional model. Our key observation is that reference models serve only t...
| Authors | Francesco Rita, Jie Zhang, Florian Tramèr |
|---|---|
| Primary category | cs.CR |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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