Find and Dig: A Privacy-Preserving Image Processing Mechanism in Deep Neural Networks for Mobile Computation

In recent years, there have been increasing demands for using deep neural networks (DNNs) to provide image processing services for mobile devices. Considering the privacy of users' images, we utilize a two-tiers DNN which deploys the shallow and deep model on mobile devices and the cloud respectively. Then we propose a novel privacy protection mechanism which is deployed on the mobile device to satisfy the differential privacy. Meanwhile, based on the convolution kernel analysis, we also propose a novel method to improve the computation efficiency of mobile devices. The highlight of our mechanism is that it not only provides customized privacy protection which can resist the attack of Generative Adversarial Network (GAN), but also improves the accuracy of the neural network model. The experimental results on the ImageNet dataset show that we have improved the top-5 accuracy of image classification by 2%-3%. Under the premise of ensuring that the accuracy of the network is not degraded, our method reduces the CPU consumption on the VGG16 and ResNet50 networks to 74.6% and 48.9%, respectively, and can reduce 90% of the memory overhead. This improvement makes it possible to enable mobile deep neural network applications.