Improved Secure Deep Neural Network Inference Offloading with Privacy-Preserving Scalar Product Evaluation for Edge Computing

Enabling deep learning inferences on resource-constrained devices is important for intelligent Internet of Things. Edge computing makes this feasible by outsourcing resource-consuming operations from IoT devices to edge devices. In such scenarios, sensitive data shall be protected while transmitted to the edge. To address this issue, one major challenge is to efficiently execute inference tasks without hampering the real-time operation of IoT applications. Existing techniques based on complex cryptographic primitives or differential privacy are limited to either efficiency or model accuracy. This paper addresses this challenge with a lightweight interactive protocol by utilizing low-latency IoT-to-edge communication links for computational efficiency. We achieve this with a new privacy-preserving scalar product evaluation technique that caters to the unique requirements of deep learning inference. As compared to the state-of-the-art, our solution offers improved trade-offs among privacy, efficiency, and utility. Experimental results on a Raspberry Pi 4 (Model B) show that our construction can achieve over 14x acceleration versus local execution for AlexNet inference over ImageNet. The proposed privacy-preserving scalar-product-evaluation technique can also be used as a general primitive in other applications.