Serpens: Privacy-Preserving Inference through Conditional Separable of Convolutional Neural Networks

Longlong Sun, Hui Li, Yanguo Peng, Jiangtao Cui

Abstract: With the extensive usage of convolutional neural networks (CNNs), privacy issues within practical applications have attracted much attention, especially when deep learning services are provided by third-party clouds. Many private inference schemes have been proposed, but their overheads are still too large. In this work, we find that the inference procedure of CNNs can be separated and performed synergistically by many parties. Following this observation, we present a pair of novel notions, namely separable and conditional separable, to tell whether a layer in CNNs can be exactly computed over multiple parties or not. Besides, we also prove that CNNs are conditionally separable. Accordingly, we propose Serpens, a private inference framework under multi-server settings. Serpens reduces the overhead of linear layers to almost zero, and now the computing bottleneck is ReLU. To address that, we design two secure ReLU protocols based on homomorphic encryption and random masks for two- and three-server settings. Experimental results show that Serpens is 78x-105x faster than the state-of-the-art private inference scheme in the two-server setting, and the superiority of Serpens is even larger in the three-server setting, only 11x-64x slower than performing the same inference over plaintext images.