  Deep neural networks are susceptible to various inference attacks as they
remember information about their training data. We design white-box inference
attacks to perform a comprehensive privacy analysis of deep learning models. We
measure the privacy leakage through parameters of fully trained models as well
as the parameter updates of models during training. We design inference
algorithms for both centralized and federated learning, with respect to passive
and active inference attackers, and assuming different adversary prior
knowledge.
  We evaluate our novel white-box membership inference attacks against deep
learning algorithms to trace their training data records. We show that a
straightforward extension of the known black-box attacks to the white-box
setting (through analyzing the outputs of activation functions) is ineffective.
We therefore design new algorithms tailored to the white-box setting by
exploiting the privacy vulnerabilities of the stochastic gradient descent
algorithm, which is the algorithm used to train deep neural networks. We
investigate the reasons why deep learning models may leak information about
their training data. We then show that even well-generalized models are
significantly susceptible to white-box membership inference attacks, by
analyzing state-of-the-art pre-trained and publicly available models for the
CIFAR dataset. We also show how adversarial participants, in the federated
learning setting, can successfully run active membership inference attacks
against other participants, even when the global model achieves high prediction
accuracies.
