  While Deep Neural Networks (DNNs) have demonstrated remarkable performance in
tasks related to perception and control, there are still several unresolved
concerns regarding the privacy of their training data, particularly in the
context of vulnerability to Membership Inference Attacks (MIAs). In this paper,
we explore a connection between the susceptibility to membership inference
attacks and the vulnerability to distillation-based functionality stealing
attacks. In particular, we propose {GLiRA}, a distillation-guided approach to
membership inference attack on the black-box neural network. We observe that
the knowledge distillation significantly improves the efficiency of likelihood
ratio of membership inference attack, especially in the black-box setting,
i.e., when the architecture of the target model is unknown to the attacker. We
evaluate the proposed method across multiple image classification datasets and
models and demonstrate that likelihood ratio attacks when guided by the
knowledge distillation, outperform the current state-of-the-art membership
inference attacks in the black-box setting.
