Secure two-party computation with homomorphic encryption (HE) protects data privacy with a formal security guarantee but suffers from high communication overhead. While previous works, e.g., Cheetah, Iron, etc, have proposed efficient HE-based protocols for different neural network (NN) operations, they still assume high precision, e.g., fixed point 37 bit, for the NN operations and ignore NNs' native robustness against quantization error. In this paper, we propose HEQuant, which features low-precision-quantization-aware optimization for the HE-based protocols. We observe the benefit of a naive combination of quantization and HE quickly saturates as bit precision goes down. Hence, to further improve communication efficiency, we propose a series of optimizations, including an intra-coefficient packing algorithm and a quantization-aware tiling algorithm, to simultaneously reduce the number and precision of the transferred data. Compared with prior-art HE-based protocols, e.g., CrypTFlow2, Cheetah, Iron, etc, HEQuant achieves $3.5\sim 23.4\times$ communication reduction and $3.0\sim 9.3\times$ latency reduction. Meanwhile, when compared with prior-art network optimization frameworks, e.g., SENet, SNL, etc, HEQuant also achieves $3.1\sim 3.6\times$ communication reduction.

翻译：基于同态加密（HE）的安全两方计算通过形式化安全保证保护数据隐私，但存在通信开销过高的问题。尽管先前工作如Cheetah、Iron等已针对不同神经网络操作提出高效HE协议，但其仍假设神经网络操作采用高精度（例如37位定点数），忽略了神经网络对量化误差的固有鲁棒性。本文提出HEQuant，其核心特征在于对基于HE的协议进行低精度量化感知优化。我们观察到，朴素结合量化与HE的收益会随比特精度降低而迅速饱和。为此，为进一步提升通信效率，我们提出了一系列优化方法，包括系数内打包算法和量化感知分块算法，以同时减少传输数据的数量和精度。与先前基于HE的协议（如CrypTFlow2、Cheetah、Iron等）相比，HEQuant实现了$3.5\sim 23.4\times$的通信量缩减和$3.0\sim 9.3\times$的延迟降低。同时，与先前网络优化框架（如SENet、SNL等）相比，HEQuant同样实现了$3.1\sim 3.6\times$的通信量缩减。