Author

Sheng-Jie Niu

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#machine learning Preprint Sep 2026

Membership Inference via Pairwise Likelihood Ratios

Pairwise Likelihood MIA (PL-MIA), a unified method that combines a Gaussian likelihood-ratio (GLR) statistic with population calibration and the Cauchy combination test, is proposed, demonstrating how statistical principles can turn noisy model outputs into more powerful, calibrated, and reproducible evidence for membe...

Sheng-Jie Niu, Ze-Bin Yun, Ye-Heng Ge et al. · 0 citations

#machine learning Preprint Sep 2026

Black-Box Membership Inference via Word-Level Probability Estimation

Word-level Probability MIA (WPMIA) is proposed, a statistically principled MIA for strict black-box privacy auditing that estimates word-level generation probabilities via Monte Carlo sampling with local kernel smoothing, then aggregates these estimates into a sequence-level likelihood estimator.

Sheng-Jie Niu, Ye-Heng Ge, Jian Huang · 0 citations