Secure multi-party computation (MPC) techniques can be used to provide data privacy when users query deep neural network (DNN) models hosted on a public cloud. State-of-the-art MPC techniques can be directly leveraged for DNN models that use simple activation functions (AFs) such as ReLU. However, DNN model architectures designed for cutting-edge applications often use complex and highly non-linear AFs. Designing efficient MPC techniques for such complex AFs is an open problem. Towards this, we propose Compact, which produces piece-wise polynomial approximations of complex AFs to enable their efficient use with state-of-the-art MPC techniques. Compact neither requires nor imposes any restriction on model training and results in near-identical model accuracy. We extensively evaluate Compact on four different machine-learning tasks with DNN architectures that use popular complex AFs SiLU, GeLU, and Mish. Our experimental results show that Compact incurs negligible accuracy loss compared to DNN-specific approaches for handling complex non-linear AFs. We also incorporate Compact in two state-of-the-art MPC libraries for privacy-preserving inference and demonstrate that Compact provides 2x-5x speedup in computation compared to the state-of-the-art approximation approach for non-linear functions -- while providing similar or better accuracy for DNN models with large number of hidden layers

翻译：暂无翻译