Deep neural networks are susceptible to various inference attacks as they remember information about their training data. We perform a comprehensive analysis of white-box privacy inference attacks on deep learning models. We measure the privacy leakage by leveraging the final model parameters as well as the parameter updates during the training and fine-tuning processes. We design the attacks in the stand-alone and federated settings, with respect to passive and active inference attackers, and assuming different adversary prior knowledge.
We design and evaluate our novel white-box membership inference attacks against deep learning algorithms to measure their training data membership leakage. 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, widely used to train deep neural networks. We 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 of a federated learning setting can run active membership inference attacks against other participants, even when the global model achieves high prediction accuracies.
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