论文标题
通过概括差距和其他模型指标量化成员推理漏洞
Quantifying Membership Inference Vulnerability via Generalization Gap and Other Model Metrics
论文作者
论文摘要
我们演示了目标模型的概括差距如何直接导致有效的确定性黑匣子成员资格推理攻击（MIA）。这提供了基于简单指标的模型对MIA的安全性的上限。此外，在预期的意义上，这种攻击仅访问了有关网络培训和性能的某些可能可获得的指标。在实验上，在许多情况下，这种攻击的精度与最先进的MIA相当。
We demonstrate how a target model's generalization gap leads directly to an effective deterministic black box membership inference attack (MIA). This provides an upper bound on how secure a model can be to MIA based on a simple metric. Moreover, this attack is shown to be optimal in the expected sense given access to only certain likely obtainable metrics regarding the network's training and performance. Experimentally, this attack is shown to be comparable in accuracy to state-of-art MIAs in many cases.
