  Membership inference (MI) attacks exploit the fact that machine learning
algorithms sometimes leak information about their training data through the
learned model. In this work, we study membership inference in the white-box
setting in order to exploit the internals of a model, which have not been
effectively utilized by previous work. Leveraging new insights about how
overfitting occurs in deep neural networks, we show how a model's idiosyncratic
use of features can provide evidence for membership to white-box
attackers---even when the model's black-box behavior appears to generalize
well---and demonstrate that this attack outperforms prior black-box methods.
Taking the position that an effective attack should have the ability to provide
confident positive inferences, we find that previous attacks do not often
provide a meaningful basis for confidently inferring membership, whereas our
attack can be effectively calibrated for high precision. Finally, we examine
popular defenses against MI attacks, finding that (1) smaller generalization
error is not sufficient to prevent attacks on real models, and (2) while
small-$\epsilon$-differential privacy reduces the attack's effectiveness, this
often comes at a significant cost to the model's accuracy; and for larger
$\epsilon$ that are sometimes used in practice (e.g., $\epsilon=16$), the
attack can achieve nearly the same accuracy as on the unprotected model.
