计算机科学
密码学
架空(工程)
密码协议
协议(科学)
秘密分享
推论
人工神经网络
密码原语
理论计算机科学
预处理器
加密
分布式计算
计算机网络
人工智能
计算机安全
医学
替代医学
病理
操作系统
作者
Hao Meng,Hongwei Li,Hanxiao Chen,Pengzhi Xing,Tianwei Zhang
标识
DOI:10.1109/tifs.2023.3262149
摘要
Private neural network inference has demonstrated great importance in various privacy-critical scenarios. However, the primary challenge remaining in prior works is that the evaluation on encrypted data levies prohibitively high run-time and communication overhead. In this work, we present FastSecNet, an efficient two-party cryptographic framework for private inference in the dealer-based pre-processing setting. Specifically, (1) FastSecNet provides an efficient ReLU protocol for the evalution of non-linear layers, which is built up on a recent advanced cryptographic primitive, function secret sharing (FSS). The core of this construction are an optimized ReLU representation and a customized FSS-based ReLU protocol. (2) For linear layer evaluation, we first propose an efficient PRG-based preprocessing protocol based on the fact that one of the inputs is uniformly random in the offline phase. Then, the online phase only communicates one element and consists of lightweight secret-sharing operations in a ring. Extensive evaluations conducted on 4 real-world datasets and 9 neural network models demonstrate that during the online phase, FastSecNet achieves 14× less runtime and 18× less communication cost compared to the state-of-the-art.
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