Artificial intelligence (AI) on edge refers to combining edge computing and AI, and enjoys the benefit of distributed structure, intelligence, and timeliness. Specifically, natural language processing model, which allows to processing language data right close to the device location within milliseconds and providing intelligent controller, have revolutionized researches. Recently, privacy concerns spiked when it is applied in healthcare, autonomous vehicles, manufacturing, etc. Secure multi-party computation has the advantage of strong security guarantee and computability over multi-sourced data. However, it is challenging to translate the timeliness benefit of Edge AI to secure deployment, as only constrained computation and storage resources are available for the edge nodes. We focus on the natural language processing (NLP), and design an efficient secure three-party computation protocol (called QuickNLP) in the semi-honest setting tolerating one corruption. Specifically, for the non-linear operations, we adopt the constant-round distributed comparison function (${\sf DCF}$) to evaluate the piecewise function efficiently with high accuracy. The proposed framework has been experimented with Python and the results show that QuickNLP could be a valuable solution for data privacy in edge computing. Specifically, compared to the existing protocol, we improve the computation costs by a factor of roughly$7\times$.

This letter investigates the inversion of rough surface parameters (the root mean square height and the correlation length) from microwave images by using deep convolutional neural networks (CNNs). Training data for the deep CNN are simulated numerically using computational electromagnetic method. As CNN is powerful in extracting image features, scattering field from rough surfaces is first converted to microwave images via interpolated fast Fourier transform and then fed into the CNN. In order to reduce overfitting, the regularization technique and dropout layer are used. The proposed CNN consists of five pairs of convolutional and maxpooling layers and two additional convolution layers for feature extraction and two fully connected layers for parameter regression. The experimental results demonstrated the feasibility using deep neural networks for the parameter inversion of rough surface from electromagnetic scattering fields. It suggests potential application of CNN for rough surface parameter inversion from microwave sensing data.