Revisiting the LiRA Membership Inference Attack Under Realistic Assumptions
Membership inference attacks (MIAs) have become the standard tool for evaluating privacy leakage in machine learning (ML). Among them, the Likelihood-Ratio Attack (LiRA) is widely regarded as the state of the art when sufficient shadow models are available. However, prior evaluations have often overstated the effectiveness of LiRA by attacking models overconfident on their training samples, calibrating thresholds on target data, assuming balanced membership priors, and/or overlooking attack reproducibility. We re-evaluate LiRA under a realistic protocol that (i) trains models using anti-overfitting (AOF) and transfer learning (TL), when applicable, to reduce overconfidence as in productio...
| Authors | Najeeb Jebreel, Mona Khalil, David Sánchez et al. |
|---|---|
| Primary category | cs.CR |
| Published | 8 March 2026 |
| Last updated at source | 8 March 2026 |
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