Can the privacy vulnerability of individual data points be assessed without retraining models or explicitly simulating attacks? We answer affirmatively by showing that exposure to membership inference attack (MIA) is fundamentally governed by a data point's influence on the learned model. We formalize this in the linear setting by establishing a theoretical correspondence between individual MIA risk and the leverage score, identifying it as a principled metric for vulnerability. This characterization explains how data-dependent sensitivity translates into exposure, without the computational burden of training shadow models. Building on this, we propose a computationally efficient generalization of the leverage score for deep learning. Empirical evaluations confirm a strong correlation between the proposed score and MIA success, validating this metric as a practical surrogate for individual privacy risk assessment.

翻译：能否在不重新训练模型或显式模拟攻击的情况下评估个体数据点的隐私脆弱性？我们通过证明成员推理攻击（MIA）的暴露程度本质上由数据点对已学习模型的影响所决定，对此给出了肯定回答。在线性设定中，我们通过建立个体MIA风险与杠杆分数之间的理论对应关系，将其形式化地定义为脆弱性的原理性度量指标。该特征化阐释了数据依赖的敏感性如何转化为暴露风险，且无需承担训练影子模型的计算负担。基于此，我们提出了一种适用于深度学习的计算高效型广义杠杆分数。实证评估证实了所提分数与MIA成功率之间的强相关性，验证了该度量作为个体隐私风险评估实用替代指标的可行性。