← 研踪 PaperTrailarXiv:2610.36458 · v1
A Simple Presentation of Robust Membership Inference Attacks
首次提交 2026-10-27 09:06
版本更新 2026-10-27 09:06
雷达观测 2026-10-29 00:35
摘要
In a membership inference attack, an attacker is given the data of a particular individual, and tries to determine if that individual was included in a particular dataset, given some kind of statistics derived from the dataset or a model trained on the dataset. Roughly, a membership inference attack is robust if it succeeds even when the statistics are highly noisy, regardless of how that noise is introduced. We give a simple presentation of robust membership inference attacks for mean estimation, covering both multivariate Gaussian distributions and binary product distributions. We also give a summary of the literature on robust membership inference, covering history, extensions, and connections to other research areas.
作者备注：34 pages, 2 figures
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v1 · 2026-10-27 09:06 · 最新观测版本
A Simple Presentation of Robust Membership Inference Attacks
In a membership inference attack, an attacker is given the data of a particular individual, and tries to determine if that individual was included in a particular dataset, given some kind of statistics derived from the dataset or a model trained on the dataset. Roughly, a membership inference attack is robust if it succeeds even when the statistics are highly noisy, regardless of how that noise is introduced. We give a simple presentation of robust membership inference attacks for mean estimation, covering both multivariate Gaussian distributions and binary product distributions. We also give a summary of the literature on robust membership inference, covering history, extensions, and connections to other research areas.
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