  The vulnerability of the Lottery Ticket Hypothesis has not been studied from
the purview of Membership Inference Attacks. Through this work, we are the
first to empirically show that the lottery ticket networks are equally
vulnerable to membership inference attacks. A Membership Inference Attack (MIA)
is the process of determining whether a data sample belongs to a training set
of a trained model or not. Membership Inference Attacks could leak critical
information about the training data that can be used for targeted attacks.
Recent deep learning models often have very large memory footprints and a high
computational cost associated with training and drawing inferences. Lottery
Ticket Hypothesis is used to prune the networks to find smaller sub-networks
that at least match the performance of the original model in terms of test
accuracy in a similar number of iterations. We used CIFAR-10, CIFAR-100, and
ImageNet datasets to perform image classification tasks and observe that the
attack accuracies are similar. We also see that the attack accuracy varies
directly according to the number of classes in the dataset and the sparsity of
the network. We demonstrate that these attacks are transferable across models
with high accuracy.
