Edge-Assisted CNN Inference over Encrypted Data for Internet of Things

Abstract

Supporting the inference tasks of convolutional neural network (CNN) on resource-constrained Internet of Things (IoT) devices in a timely manner has been an outstanding challenge for emerging smart systems. To mitigate the burden on IoT devices, one prevalent solution is to offload the CNN inference tasks to the public cloud. However, this “offloading-to-cloud” solution may cause privacy breach since the offloaded data can contain sensitive information. For privacy protection, the research community has resorted to advanced cryptographic primitives to support CNN inference over encrypted data. Nevertheless, these attempts are limited by the real-time performance due to the heavy IoT computational overhead brought by cryptographic primitives.