FlagThis

Abstract

Side-channel attacks can recover neural network parameters from physical signals, even on commercial edge accelerators. Existing defenses require changes to hardware, instruction set, or compiler, and cannot be deployed on fixed-function platforms such as TPUs. We present the first training-time defense that protects models on off-the-shelf TPUs without modifying the hardware or firmware. Our approach trains multiple functionally equivalent parameter versions per layer and randomly composes them at inference. This reduces the correlation that sidechannel attacks rely on while preserving model accuracy. We enforce diversity between parameter versions by adding a regularization term in the loss function during training. We show that this diversity increases leakage variance while leaving the mean signal unchanged, which provably reduces the signal-to-noise ratio exploited by attackers. We derive theoretical bounds that relate leakage to the number of parameter versions and their pairwise distance, and provide a simple calibration method to predict leakage for new configurations before deployment or side-channel measurements. We implement our method on a Google Edge TPU and evaluate it on representative and real-world models. Our defense, in a high-diversity configuration, can hide leakage by reducing the Test Vector Leakage Assessment t-score below the standard leakage detection threshold of 4.5 for the majority of a neural network, with less than 1% accuracy change and moderate overhead. Our results thus show, for the first time, that training-time defenses can provide practical side-channel protection for widely deployed AI hardware.