  In Member Inference (MI) attacks, the adversary try to determine whether an
instance is used to train a machine learning (ML) model. MI attacks are a major
privacy concern when using private data to train ML models. Most MI attacks in
the literature take advantage of the fact that ML models are trained to fit the
training data well, and thus have very low loss on training instances. Most
defenses against MI attacks therefore try to make the model fit the training
data less well. Doing so, however, generally results in lower accuracy. We
observe that training instances have different degrees of vulnerability to MI
attacks. Most instances will have low loss even when not included in training.
For these instances, the model can fit them well without concerns of MI
attacks. An effective defense only needs to (possibly implicitly) identify
instances that are vulnerable to MI attacks and avoids overfitting them. A
major challenge is how to achieve such an effect in an efficient training
process. Leveraging two distinct recent advancements in representation
learning: counterfactually-invariant representations and subspace learning
methods, we introduce a novel Membership-Invariant Subspace Training (MIST)
method to defend against MI attacks. MIST avoids overfitting the vulnerable
instances without significant impact on other instances. We have conducted
extensive experimental studies, comparing MIST with various other
state-of-the-art (SOTA) MI defenses against several SOTA MI attacks. We find
that MIST outperforms other defenses while resulting in minimal reduction in
testing accuracy.
