As a promising data-driven technology, deep learning has been widely employed in a
variety of Internet-of-Things (IoT) applications. Examples include automated navigation,
telemedicine, and smart home. To protect the data privacy of deep-learning-based IoT
applications, a few privacy-preserving approaches have also been exploited, designed, and
implemented in various scenarios. However, state-of-the-art works are still defective in
accuracy, efficiency, and functionality. In this article, we propose the privacy-aware and …