  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.
