  The large model size, high computational operations, and vulnerability
against membership inference attack (MIA) have impeded deep learning or deep
neural networks (DNNs) popularity, especially on mobile devices. To address the
challenge, we envision that the weight pruning technique will help DNNs against
MIA while reducing model storage and computational operation. In this work, we
propose a pruning algorithm, and we show that the proposed algorithm can find a
subnetwork that can prevent privacy leakage from MIA and achieves competitive
accuracy with the original DNNs. We also verify our theoretical insights with
experiments. Our experimental results illustrate that the attack accuracy using
model compression is up to 13.6% and 10% lower than that of the baseline and
Min-Max game, accordingly.
