This work presents Origami, which provides privacy-preserving inference for large deep
neural network (DNN) models through a combination of enclave execution, cryptographic …

The Internet of Things (IoT) devices have been increasingly deployed in smart homes and
smart buildings to monitor and control their environments. The Internet traffic data produced …

Collaborative inference has recently emerged as an attractive framework for applying deep
learning to Internet of Things (IoT) applications by splitting a DNN model into several subpart …

The ever-growing advances of deep learning in many areas including vision,
recommendation systems, natural language processing, etc., have led to the adoption of …

This article proposes a sensor data anonymization model that is trained on decentralized
data and strikes a desirable trade-off between data utility and privacy, even in …

Deep Learning has recently become hugely popular in machine learning for its ability to
solve end-to-end learning systems, in which the features and the classifiers are learned …

Deep learning sits at the forefront of many on-going advances in a variety of learning tasks.
Despite its supremacy in accuracy under benign environments, Deep learning suffers from …

With the wide applications of deep neural networks (DNNs) in various fields, current
research shows their serious security risks due to the lack of privacy protection. Observing …

Privacy protection in electronic healthcare applications is an important consideration due to
the sensitive nature of personal health data. Internet of Health Things (IoHT) networks have …

It is of crucial importance to simultaneously protect against sensitive attributes in data while
building predictive models. In this paper, we tackle the problem of learning representations …