  Hierarchical text classification consists in classifying text documents into
a hierarchy of classes and sub-classes. Although artificial neural networks
have proved useful to perform this task, unfortunately they can leak training
data information to adversaries due to training data memorization. Using
differential privacy during model training can mitigate leakage attacks against
trained models, enabling the models to be shared safely at the cost of reduced
model accuracy. This work investigates the privacy-utility trade-off in
hierarchical text classification with differential privacy guarantees, and
identifies neural network architectures that offer superior trade-offs. To this
end, we use a white-box membership inference attack to empirically assess the
information leakage of three widely used neural network architectures. We show
that large differential privacy parameters already suffice to completely
mitigate membership inference attacks, thus resulting only in a moderate
decrease in model utility. More specifically, for large datasets with long
texts we observed Transformer-based models to achieve an overall favorable
privacy-utility trade-off, while for smaller datasets with shorter texts
convolutional neural networks are preferable.
