The increasing prominence of deep learning applications and reliance on
personalized data underscore the urgent need to address privacy
vulnerabilities, particularly Membership Inference Attacks (MIAs). Despite
numerous MIA studies, significant knowledge gaps persist, particularly
regarding the impact of hidden features (in isolation) on attack efficacy and
insufficient justification for the root causes of attacks based on raw data
features. In this paper, we aim to address these knowledge gaps by first
exploring statistical approaches to identify the most informative neurons and
quantifying the significance of the hidden activations from the selected
neurons on attack accuracy, in isolation and combination. Additionally, we
propose an attack-driven explainable framework by integrating the target and
attack models to identify the most influential features of raw data that lead
to successful membership inference attacks. Our proposed MIA shows an
improvement of up to 26% on state-of-the-art MIA.

通过统计方法，本文旨在解决隐含特征对攻击效果的影响以及基于原始数据特征的攻击根本原因不足的问题，并提出了一个攻击驱动的可解释框架，通过集成目标和攻击模型，识别导致成功成员推理攻击的原始数据上最具影响力的特征。我们提出的攻击显示了与最先进的攻击技术相比高达 26％的改进。