Telepathic Headache: Mitigating Cache Side-Channel Attacks on Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are the target of several side-channel attacks aiming at recovering their parameters and hyper-parameters. Attack vectors include monitoring of the cache, power consumption analysis and execution time measurements. These attacks often rely on the knowledge of a certain - large - set of hyper-parameters among which the victim model lies. The goal of the potential attacker is then to reduce that search space or even deduce the correct architecture. One such attack, Cache Telepathy by Yan et al., monitors access to a common matrix multiplication algorithm, GeMM (Generalized Matrix Multiply), in order to determine the victim model's hyper-parameters. In this paper, we propose to change the order in which the computations are made and add randomness to the said computations in order to mitigate Cache Telepathy. The security analysis of our protection shows that the Cache Telepathy attack on a protected VGG-16 has an increased search space: from 16 to 2(22).