Deep learning is one of the advanced approaches of machine learning, and has attracted a
growing attention in the recent years. It is used nowadays in different domains and …

By moving computing resources to the network logical edge, mobile edge computing is
promising for providing low-latency computing services to mobile users, and task offloading …

A wide variety of deep neural applications increasingly rely on the cloud to perform their
compute-heavy inference. This common practice requires sending private and privileged …

Deep neural networks are widely deployed on edge devices (eg., for computer vision and
speech recognition). Users either perform the inference locally (ie., edge-based) or send the …

When receiving machine learning services from the cloud, the provider does not need to
receive all features; in fact, only a subset of the features are necessary for the target …

In recent years, deep learning in healthcare applications has attracted considerable
attention from research community. They are deployed on powerful cloud infrastructures to …

While cloud-based deep learning benefits for high-accuracy inference, it leads to potential
privacy risks when exposing sensitive data to untrusted servers. In this paper, we work on …

With the emergence of large foundational models, model-serving systems are becoming
popular. In such a system, users send the queries to the server and specify the desired …

A wide variety of DNN applications increasingly rely on the cloud to perform their huge
computation. This heavy trend toward cloud-hosted inference services raises serious privacy …

Visual analysis tasks, including crowd management, often require resource-intensive
machine learning models, posing challenges for deployment on edge hardware …