论文标题
NASS：通过神经体系结构搜索优化安全推理
NASS: Optimizing Secure Inference via Neural Architecture Search
论文作者
论文摘要
由于越来越多的隐私问题，基于神经网络（NN）的安全推理（SI）方案同时隐藏了客户端输入和服务器模型，吸引了主要的研究兴趣。尽管现有作品着重于为基于NN的SI开发安全协议，但在这项工作中，我们采用了另一种方法。我们提出了NASS，这是一个集成框架，用于搜索专门为SI设计的量身定制的NN体系结构。特别是，我们建议将加密协议建模为具有相关奖励功能的设计元素。然后，在与预测的超参数的联合优化中采用了特征模型，以识别平衡预测准确性和执行效率的最佳NN体系结构。在实验中，证明我们可以通过使用NASS来实现两全其美的最佳状态，在这种情况下，预测准确性可以从81.6％提高到84.6％，而在CIFAR-10数据集上，推理运行时则将推理运行时降低2倍，通信带宽减少了1.9倍。
Due to increasing privacy concerns, neural network (NN) based secure inference (SI) schemes that simultaneously hide the client inputs and server models attract major research interests. While existing works focused on developing secure protocols for NN-based SI, in this work, we take a different approach. We propose NASS, an integrated framework to search for tailored NN architectures designed specifically for SI. In particular, we propose to model cryptographic protocols as design elements with associated reward functions. The characterized models are then adopted in a joint optimization with predicted hyperparameters in identifying the best NN architectures that balance prediction accuracy and execution efficiency. In the experiment, it is demonstrated that we can achieve the best of both worlds by using NASS, where the prediction accuracy can be improved from 81.6% to 84.6%, while the inference runtime is reduced by 2x and communication bandwidth by 1.9x on the CIFAR-10 dataset.
