This's code, for "Practical Blind Membership Inference Attack via Differential Comparison" , which is I have learnt yet.
First, we should run TargetModel.py
to train a target model. Then, we run ShadowModel.py
to train a shadow model for meeting some needs. After do like this, we have done the preliminary works which can let us do BlindMIA.
We have nine membership inference attack to use. Let's tell you how to use it and what about its utility:
BlindMI_Diff_W: It's a membership inference attack with non-memership-set. And it use MMD to estimate a data which is in train-set or not.