SparseLeakyNets: Classification Prediction Attack Over Sparsity-Aware Embedded Neural Networks Using Timing Side-Channel Information

Abstract

This letter explores security vulnerabilities in sparsity-aware optimizations for Neural Network (NN) platforms, specifically focusing on timing side-channel attacks introduced by optimizations such as skipping sparse multiplications. We propose a classification prediction attack that utilizes this timing side-channel information to mimic the NN's prediction outcomes. Our techniques were demonstrated for CIFAR-10, MNIST, and biomedical classification tasks using diverse dataflows and processing loads in timing models. The demonstrated results could predict the original classification decision with high accuracy.