  As machine learning expanding application, there are more and more
unignorable privacy and safety issues. Especially inference attacks against
Machine Learning models allow adversaries to infer sensitive information about
the target model, such as training data, model parameters, etc. Inference
attacks can lead to serious consequences, including violating individuals
privacy, compromising the intellectual property of the owner of the machine
learning model. As far as concerned, researchers have studied and analyzed in
depth several types of inference attacks, albeit in isolation, but there is
still a lack of a holistic rick assessment of inference attacks against machine
learning models, such as their application in different scenarios, the common
factors affecting the performance of these attacks and the relationship among
the attacks. As a result, this paper performs a holistic risk assessment of
different inference attacks against Machine Learning models. This paper focuses
on three kinds of representative attacks: membership inference attack,
attribute inference attack and model stealing attack. And a threat model
taxonomy is established. A total of 12 target models using three model
architectures, including AlexNet, ResNet18 and Simple CNN, are trained on four
datasets, namely CelebA, UTKFace, STL10 and FMNIST.
