Membership inference attacks (MIAs) threaten the privacy of machine learning (ML) training data by determining whether a sample was used to train a target model. Existing MIAs rely on model outputs, r...
arXiv:2602. 18934v2 Announce Type: replace Abstract: Membership inference attacks (MIAs) threaten the privacy of machine learning models by revealing whether a specific data point was used during training.
By Abdullah Caglar Oksuz, Anisa Halimi, Erman Ayday
The paper introduces Pairwise Likelihood MIA (PL‑MIA), a unified membership inference attack that combines a Gaussian likelihood‑ratio statistic with population calibration and the Cauchy combination test. PL‑MIA generates p‑values from pairwise comparisons between a query point and reference points, then aggregates these continuous signals using the Cauchy test to preserve evidence strength. Experiments show that PL‑MIA surpasses strong baselines, boosting true positive rates by over 25% in low‑false‑positive settings, thereby validating the theoretical advantages of the proposed statistical framework.
By Shengjie Niu, Zebin Yun, Yeheng Ge, Jian Huang
The paper introduces a method to estimate a model’s vulnerability to the Likelihood Ratio Attack (LiRA) without training reference models, using only the target model’s train and test loss distributions. It shows that LiRA’s per‑sample signal can be decomposed into a variance‑ratio term and a residual mean‑shift term, and that different loss‑distribution shapes dictate which reference‑free proxy to use. Two proxies are presented: the LOSS attack TNR for heavy‑tailed losses and the LOSS attack AUC for symmetric losses, both achieving low RMSE in predicting LiRA TPR across multiple architectures and datasets.
By Euodia Dodd, Nata\v{s}a Kr\v{c}o, Igor Shilov, Matthew Wicker, Yves-Alexandre de Montjoye
The paper introduces AutoMIA, a framework that uses large language model agents to automatically design and implement new membership inference attack (MIA) signal computations. By systematically exploring a wide range of attack strategies, AutoMIA discovers novel MIAs tailored to specific target models and datasets, achieving up to a 0.18 absolute improvement in AUC over existing methods. This demonstrates that LLM agents can serve as an effective and scalable approach for creating state‑of‑the‑art MIAs.
By Toan Tran, Olivera Kotevska, Li Xiong
The paper examines the reliability of membership inference attack (MIA) vulnerability evaluation. It identifies two weaknesses: finite‑sample bias from sampling shadow datasets from a fixed superset, and miscalibration when aggregating true positive rates across individuals at very low false positive rates. The authors propose simple fixes that avoid extra computational cost and suggest further improvements with additional computation.
By Joonas J\"alk\"o, Gauri Pradhan, Ossi R\"ais\"a, Antti Honkela