PriPro: Towards Effective Privacy Protection on Edge-Cloud System running DNN Inference

The huge computation demand for deep learning models and limited computation resources on the edge devices calls for the cooperation between the edge device and cloud service. On a typical edge-cloud system accommodating DNN inference, a deep model is split into two partial models running on the edge device and the cloud service, respectively. The two partial models collaborate closely to satisfy the DNN inference requested by the user. However, user's privacy is vulnerable when transferring the intermediate results generated by the partial model at edge device to cloud service. Existing research works rely on metrics that are either impractical or insufficient to measure the effectiveness of privacy protection methods in the above scenario, especially from a single input aspect. In this paper, we first thoroughly analyze the state-of-the-art methods and drawbacks of existing methods from the aspects of both evaluation metrics and proposed techniques. Then, we propose a new metric system, including privacy accuracy (PA) and privacy index (PI), that can accurately measure the effectiveness of privacy protection methods. Furthermore, we propose PriPro, a privacy protection method that can dynamically inject noise to the intermediate results at various layers regarding the input features through the self-attention mechanism. The experiment results demonstrate our method outperforms existing methods for protecting user privacy on deep models such as AlexNet, VGG, and ResNet.