Outsourced inference service has enormously promoted the popularity of deep learning, and
helped users to customize a range of personalized applications. However, it also entails a …

With the increased usage of AI accelerators on mobile and edge devices, on-device
machine learning (ML) is gaining popularity. Thousands of proprietary ML models are being …

Outsourcing deep neural networks (DNNs) inference tasks to an untrusted cloud raises data
privacy and integrity concerns. While there are many techniques to ensure privacy and …

During the past decade, deep learning has achieved excellent results in many classic
machine learning problems, such as face recognition, spam detection, and financial …

Hardware-enclaves that target complex CPU designs compromise both security and
performance. Programs have little control over micro-architecture, which leads to side …

Many artificial intelligence applications based on convolutional neural networks are directly
deployed on mobile devices to avoid network unavailability and user privacy leakage …

Current research on machine learning-based intelligent routing focuses on algorithm design
and performance optimization. How to deploy it in practice remains a pressing issue …

Machine learning as a service (MLaaS) offers users the benefit of training state-of-the-art
neural network models on fast hardware with low costs. However, it also brings security …

An edge computing marketplace could enable IoT devices (Outsourcers) to outsource
computation to any participating node (Contractors) in their proximity. In return, these nodes …

Deep neural networks are widely deployed in a variety of application areas to provide real-
time inference services, such as mobile phones, autonomous vehicles and industrial …