FORESEEN: Towards Differentially Private Deep Inference for Intelligent Internet of Things

In state-of-the-art deep learning, centralized deep learning forces end devices to pool their data in the cloud in order to train a global model on the joint data, while distributed deep learning requires a parameter server to mediate the training process among multiple end devices. However, none of these architectures scale gracefully to large-scale privacy and time-sensitive IoT applications. Therefore, we are motivated to propose a FOg-based pRivacy prEServing dEep lEarNing framework named FORESEEN, so as to achieve scalable, accurate yet private analytics. In FORESEEN, the intermediate fog nodes and the cloud collaboratively perform noisy training of deep neural networks (DNNs), while each end device and its connected fog node collaboratively perform fast, private yet accurate inference. To enhance robustness and ensure privacy, we put forward a collaborative noisy training algorithm and develop a novel representation perturber to perturb the extracted features by combining random projection, random noise addition and data nullification. To meet the required constraints of accuracy, memory and energy in IoT end devices, we build deep models with mixed-precision. Through these sophisticated designs, FORESEEN is able to not only preserve privacy but also maintain comparable inference performance. Extensive experimental results under different datasets, different inference schemes and different noise addition strategies validate the effectiveness of FORESEEN. Moreover, FORESEEN is capable of reducing the communication cost and providing inherent support for robustness and scalability.