  Inference attacks against Machine Learning (ML) models allow adversaries to
learn sensitive information about training data, model parameters, etc. While
researchers have studied, in depth, several kinds of attacks, they have done so
in isolation. As a result, we lack a comprehensive picture of the risks caused
by the attacks, e.g., the different scenarios they can be applied to, the
common factors that influence their performance, the relationship among them,
or the effectiveness of possible defenses. In this paper, we fill this gap by
presenting a first-of-its-kind holistic risk assessment of different inference
attacks against machine learning models. We concentrate on four attacks --
namely, membership inference, model inversion, attribute inference, and model
stealing -- and establish a threat model taxonomy.
  Our extensive experimental evaluation, run on five model architectures and
four image datasets, shows that the complexity of the training dataset plays an
important role with respect to the attack's performance, while the
effectiveness of model stealing and membership inference attacks are negatively
correlated. We also show that defenses like DP-SGD and Knowledge Distillation
can only mitigate some of the inference attacks. Our analysis relies on a
modular re-usable software, ML-Doctor, which enables ML model owners to assess
the risks of deploying their models, and equally serves as a benchmark tool for
researchers and practitioners.
