Learning to Prevent Input Leakages in the Mobile Cloud Inference

Powered by machine learning services in the cloud, numerous learning-driven mobile applications are gaining popularity in the market. As deep learning tasks are mostly computation-intensive, it has become a trend to process raw data on devices and send the deep neural network (DNN) features to the cloud, where the features are further processed to return final results. However, there is always an unexpected leakage with the release of features, by which an adversary could infer much information on the original data. We propose a privacy-preserving framework on top of the mobile cloud infrastructure from the perspective of DNN structures. Our framework aims to learn a policy to modify the base DNNs to prevent information leakage while maintaining high inference accuracy. The policy can also be readily transferred to large-size DNNs and large-scale datasets to speed up learning. Extensive evaluations on a variety of DNNs have shown that our framework successfully finds privacy-preserving DNN structures to defend privacy attacks.