ShadowNet: A Secure and Efficient System for On-device Model Inference

On-device machine learning (ML) is getting more and more popular as fast evolving AI accelerators emerge on mobile and edge devices. Consequently, thousands of proprietary ML models are being deployed on billions of untrusted devices, raising a serious security concern about the model privacy. However, how to protect the model privacy without losing access to the AI accelerators is a challenging problem. This paper presents a novel on-device model inference system called ShadowNet, which protects the model privacy with TEE while securely outsourcing the heavy linear layers of the model onto the hardware accelerators without trusting them. ShadowNet achieves it by transforming the weights of the linear layers before outsourcing them and restoring the results inside the TEE. The nonlinear layers are also kept secure inside the TEE. The transformation of the weights and the restoration of the results are designed in a way that can be implemented efficiently. We have built a ShadowNet prototype based on TensorFlow Lite and applied it on three popular CNNs including AlexNet, MiniVGG and MobileNets. Our evaluation shows that the ShadowNet transformed models have comparable performance with the original models, offering a promising solution for secure on-device model inference.