Abstract:
Machine learning models, especially deep neural networks have been shown to reveal membership information of inputs in the training data.
Such membership inference attacks are a serious
privacy concern, for example, patients providing
medical records to build a model that detects HIV
would not want their identity to be leaked. Further, we show that the attack accuracy amplifies when the model is used to predict samples that
come from a different distribution than the training set, which is often the case in real world applications. Therefore, we propose the use of causal
learning approaches where a model learns the
causal relationship between the input features and
the outcome. An ideal causal model is known
to be invariant to the training distribution and
hence generalizes well to shifts between samples
from the same distribution and across different
distributions. First, we prove that models learned
using causal structure provide stronger differential privacy guarantees than associational models under reasonable assumptions. Next, we show
that causal models trained on sufficiently large
samples are robust to membership inference attacks across different distributions of datasets and those trained on smaller sample sizes always have
lower attack accuracy than corresponding associational models. Finally, we confirm our theoretical claims with experimental evaluation on 4 datasets
with moderately complex Bayesian networks and
an image dataset of colored MNIST. We observe that neural
network-based associational models exhibit upto
80% attack accuracy under different test distributions and sample sizes whereas causal models exhibit attack accuracy close to a random guess.
Our results confirm the value of the generalizability of causal models in reducing susceptibility to privacy attacks.
