Re-aligning Shadow Models can Improve White-box Membership Inference Attacks
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Machine learning models have been shown to leak sensitive information about their training datasets. As models are being increasingly used, on devices, to automate tasks and power new applications, there have been concerns that such white-box access to its parameters, as opposed to the black-box setting which only provides query access to the model, increases the attack surface. Directly extending the shadow modelling technique from the black-box to the white-box setting has been shown, in general, not to perform better than black-box only attacks. A key reason is misalignment, a known characteristic of deep neural networks. We here present the first systematic analysis of the causes of misalignment in shadow models and show the use of a different weight initialisation to be the main cause of shadow model misalignment. Second, we extend several re-alignment techniques, previously developed in the model fusion literature, to the shadow modelling context, where the goal is to re-align the layers of a shadow model to those of the target model.We show re-alignment techniques to significantly reduce the measured misalignment between the target and shadow models. Finally, we perform a comprehensive evaluation of white-box membership inference attacks (MIA). Our analysis reveals that (1) MIAs suffer from misalignment between shadow models, but that (2) re-aligning the shadow models improves, sometimes significantly, MIA performance. On the CIFAR10 dataset with a false positive rate of 1%, white-box MIA using re-aligned shadow models improves the true positive rate by 4.5%.Taken together, our results highlight that on-device deployment increase the attack surface and that the newly available information can be used by an attacker.
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