HWGN2: Side-channel Protected Neural Networks through Secure and Private Function Evaluation

TL;DR

This paper introduces HWGN2, a hardware-implemented neural network accelerator on FPGA that enhances side-channel resistance using cryptographic secure function evaluation, offering a trade-off between security and resource efficiency.

Contribution

HWGN2 applies garbled circuits for neural network acceleration, achieving significant reductions in logic and memory use while providing side-channel and input privacy protections.

Findings

Achieves up to 62.5x fewer logical units

Uses 66x less memory than state-of-the-art

Demonstrates side-channel resistance via TVLA tests

Abstract

Recent work has highlighted the risks of intellectual property (IP) piracy of deep learning (DL) models from the side-channel leakage of DL hardware accelerators. In response, to provide side-channel leakage resiliency to DL hardware accelerators, several approaches have been proposed, mainly borrowed from the methodologies devised for cryptographic implementations. Therefore, as expected, the same challenges posed by the complex design of such countermeasures should be dealt with. This is despite the fact that fundamental cryptographic approaches, specifically secure and private function evaluation, could potentially improve the robustness against side-channel leakage. To examine this and weigh the costs and benefits, we introduce hardware garbled NN (HWGN2), a DL hardware accelerator implemented on FPGA. HWGN2 also provides NN designers with the flexibility to protect their IP in real-time applications, where hardware resources are heavily constrained, through a hardware-communication cost trade-off. Concretely, we apply garbled circuits, implemented using a MIPS architecture that achieves up to 62.5x fewer logical and 66x less memory utilization than the state-of-the-art approaches at the price of communication overhead. Further, the side-channel resiliency of HWGN2 is demonstrated by employing the test vector leakage assessment (TVLA) test against both power and electromagnetic side-channels. This is in addition to the inherent feature of HWGN2: it ensures the privacy of users' input, including the architecture of NNs. We also demonstrate a natural extension to the malicious security modeljust as a by-product of our implementation.