A Trusted Inference Mechanism for Edge Computing Based on Post-Quantum Encryption

Edge computing is a computing framework that offers fewer computing resources compared to cloud computing but brings enterprise applications closer to data sources like Internet of Things (IoT) devices or local edge servers. This proximity to data sources brings significant business benefits such as faster insights, improved response time, and better bandwidth availability. Consequently, edge computing technologies have witnessed widespread adoption. Edge computing faces significant security challenges due to factors like limited cost, volume, and power consumption, as well as the potential risk of quantum computers breaking conventional cryptographic systems. Limitation of computing power in edge computing nodes further complicates the system architecture to achieve secure data transmission. To overcome these challenges, this paper proposes a lightweight trusted inference mechanism specifically designed for edge computing workloads. The primary objective of this mechanism is to establish a balance between security and bandwidth by incorporating a post-quantum encryption to the edge computing devices. This is achieved through sharing convolution and encryption hardware at the edge while encrypting and transmitting intermediate results of inference computations to the cloud, thereby minimizing resource usage and transmission bandwidth requirements. The FPGA platform is utilized to implement a template hardware design, which demonstrated comparable resource consumption to the stateof-art work. Simulation results indicate that our mechanism achieves a 45.3% reduction in LUTs and a 55.5% reduction in DFFs within the hardware, while introducing only a marginal software slowdown of 0.39%.