LAUSR

Neural network (NN) inference services enrich many applications, like image classification, object recognition, facial verification, and more. These NN inference services are increasingly becoming an essential offering from cloud computing providers, where end-users’ data are offloaded to the cloud for inference under a customized model. However, current cloud-based inference services operate on clear inputs and NN models, raising paramount privacy concerns. Individual user data may contain private information that should always remain confidential. Meanwhile, the NN model is deemed proprietary to the model owner as model training requires substantial resources. In this article, we present, tailor, and evaluate Sonic, a lightweight secure NN inference service delegated in the cloud. Sonic leverages the cloud computing paradigm to fully outsource the secure inference, freeing end devices and model owners from being actively online for assistance. Sonic guards both user input and model privacy along the whole service flow. We design a series of secure and efficient NN layer functions purely using lightweight cryptographic primitives. Extensive evaluations demonstrate that Sonic achieves up to $60\times$60× bandwidth saving in online inference compared to prior art.