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arXiv · 2201.06699

AESPA: Accuracy Preserving Low-degree Polynomial Activation for Fast Private Inference

Jaiyoung Park·Michael Jaemin Kim·Wonkyung Jung·Jung Ho Ahn
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Abstract

Hybrid private inference (PI) protocol, which synergistically utilizes both multi-party computation (MPC) and homomorphic encryption, is one of the most prominent techniques for PI. However, even the state-of-the-art PI protocols are bottlenecked by the non-linear layers, especially the activation functions. Although a standard non-linear activation function can generate higher model accuracy, it must be processed via a costly garbled-circuit MPC primitive. A polynomial activation can be processed via Beaver's multiplication triples MPC primitive but has been incurring severe accuracy drops so far. In this paper, we propose an accuracy preserving low-degree polynomial activation function (AESPA) that exploits the Hermite expansion of the ReLU and basis-wise normalization. We apply AESPA to popular ML models, such as VGGNet, ResNet, and pre-activation ResNet, to show an inference accuracy comparable to those of the standard models with ReLU activation, achieving superior accuracy over prior low-degree polynomial studies. When applied to the all-RELU baseline on the state-of-the-art Delphi PI protocol, AESPA shows up to 42.1x and 28.3x lower online latency and communication cost.

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Jaiyoung Park, Michael Jaemin Kim, Wonkyung Jung, Jung Ho Ahn. 2022-01-18. AESPA: Accuracy Preserving Low-degree Polynomial Activation for Fast Private Inference. https://arxiv.org/abs/2201.06699

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