The growth of the Machine-Learning-As-A-Service (MLaaS) market has highlighted clients'
data privacy and security issues. Private inference (PI) techniques using cryptographic …

Abstract The growth of Graph Convolution Network (GCN) model sizes has revolutionized
numerous applications, surpassing human performance in areas such as personal …

While deep learning has shown superior performance in various intelligent tasks, it is still a
challenging problem to deploy sophisticated models on resource-limited edge devices. Filter …

Graph Convolutional Networks (GCNs) are pivotal in extracting latent information from graph
data across various domains, yet their acceleration on mainstream GPUs is challenged by …

The increasing diffusion of tiny wearable devices and, at the same time, the advent of
machine learning techniques that can perform sophisticated inference, represent a valuable …

With the constant increase of the number of quantum bits (qubits) in the actual quantum
computers, implementing and accelerating the prevalent deep learning on quantum …

The proliferation of deep learning (DL) has led to the emergence of privacy and security
concerns. To address these issues, secure Two-party computation (2PC) has been …

Full-waveform inversion (FWI) plays a vital role in geoscience to explore the subsurface. It
utilizes the seismic wave to image the subsurface velocity map. As the machine learning …

Security has always been a critical issue in machine learning (ML) applications. Due to the
high cost of model training–such as collecting relevant samples, labeling data, and …

Two-party computation (2PC) is promising to enable privacy-preserving deep learning (DL).
However, the 2PC-based privacy-preserving DL implementation comes with high …