Two-party computation (2PC) is promising to enable privacy-preserving deep learning (DL). However, the 2PC-based privacy-preserving DL implementation comes with high comparison protocol overhead from the non-linear operators. This work presents PASNet, a novel systematic framework that enables low latency, high energy efficiency & accuracy, and security-guaranteed 2PC-DL by integrating the hardware latency of the cryptographic building block into the neural architecture search loss function. We develop a cryptographic hardware scheduler and the corresponding performance model for Field Programmable Gate Arrays (FPGA) as a case study. The experimental results demonstrate that our light-weighted model PASNet-A and heavily-weighted model PASNet-B achieve 63 ms and 228 ms latency on private inference on ImageNet, which are 147 and 40 times faster than the SOTA CryptGPU system, and achieve 70.54% & 78.79% accuracy and more than 1000 times higher energy efficiency.
@article{arxiv.2306.15513,
title = {PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deployment},
author = {Hongwu Peng and Shanglin Zhou and Yukui Luo and Nuo Xu and Shijin Duan and Ran Ran and Jiahui Zhao and Chenghong Wang and Tong Geng and Wujie Wen and Xiaolin Xu and Caiwen Ding},
journal= {arXiv preprint arXiv:2306.15513},
year = {2023}
}
Comments
DAC 2023 accepeted publication, short version was published on AAAI 2023 workshop on DL-Hardware Co-Design for AI Acceleration: RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference