  Sparse neural networks are mainly motivated by ressource efficiency since
they use fewer parameters than their dense counterparts but still reach
comparable accuracies. This article empirically investigates whether sparsity
could also improve the privacy of the data used to train the networks. The
experiments show positive correlations between the sparsity of the model, its
privacy, and its classification error. Simply comparing the privacy of two
models with different sparsity levels can yield misleading conclusions on the
role of sparsity, because of the additional correlation with the classification
error. From this perspective, some caveats are raised about previous works that
investigate sparsity and privacy.
