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In the past decade, we have witnessed an exponential growth of deep learning models, platforms, and applications. While existing DL applications and Machine Learning as a service (MLaaS) frameworks assume fully trusted models, the need for privacy-preserving DNN evaluation arises. In a secure multi-party computation scenario, both the model and the data are considered proprietary, i.e., the model owner does not want to reveal the highly valuable DL model to the user, while the user does not wish to disclose their private data samples either. Conventional privacy-preserving deep learning solutions ask the users to send encrypted samples to the model owners, who must handle the heavy lifting of ciphertext-domain computation with homomorphic encryption. In this paper, we present a novel solution, namely, PrivDNN, which (1) offloads the computation to the user side by sharing an encrypted deep learning model with them, (2) significantly improves the efficiency of DNN evaluation using partial DNN encryption, (3) ensures model accuracy and model privacy using a core neuron selection and encryption scheme. Experimental results show that PrivDNN reduces privacy-preserving DNN inference time and memory requirement by up to 97% while maintaining model performance and privacy. Codes can be found at https://github.com/LiangqinRen/PrivDNN

The prevalent point cloud compression (PCC) standards of today are utilized to encode various types of point cloud data, allowing for reasonable bandwidth and storage usage. With increasing demand for high-fidelity three-dimensional (3D) models for a large variety of applications, including immersive visual communication, Augmented reality (AR) and Virtual Reality (VR), navigation, autonomous driving, and smart city, point clouds are seeing increasing usage and development to meet the increasing demands. However, with the advancements in 3D modelling and sensing, the amount of data required to accurately depict such representations and models is likewise ballooning to increasingly large proportions, leading to the development and standardization of the point cloud compression standards. In this article, we provide an overview of some topical and popular MPEG point cloud compression (PCC) standards. We discuss the development and applications of the Geometry-based PCC (G-PCC) and Video-based PCC (V-PCC) standards as they escalate in importance in an era of virtual reality and machine learning. Finally, we conclude our article describing the future research directions and applications of the PCC standards of today.

Due to the widespread applications of high-dimensional representations in many fields, the three-dimension (3D) display technique is increasingly being used for commercial purpose in a holographic-like and immersive demonstration. However, the visual discomfort and fatigue of 3D head mounts demonstrate the limits of usage in the sphere of marketing. The compressive light field (CLF) display is capable of providing binocular and motion parallaxes by stacking multiple liquid crystal screens without any extra accessories. It leverages optical viewpoint fusion to bring an immersive and visual-pleasing experience for viewers. Unfortunately, its practical application has been limited by processing complexity and reconstruction performance. In this paper, we propose a dual-guided learning-based factorization on polarization-based CLF display with depth-assisted calibration (DAC). This substantially improves the visual performance of factorization in real-time processing. In detail, we first take advantage of a dual-guided network structure under the constraints of reconstructed and viewing images. Additionally, by utilizing the proposed DAC, we distribute each pixel on displayed screens following the real depth. Furthermore, the subjective performance is increased by using a Gauss-distribution-based weighting (GDBW) toward the concentration of the observer’s angular position. Experimental results illustrate the improved performance in qualitative and quantitative aspects over other competitive methods. A CLF prototype is assembled to verify the practicality of our factorization.