The newly emerged machine learning (eg, deep learning) methods have become a strong
driving force to revolutionize a wide range of industries, such as smart healthcare, financial …

A large number of researchers, academia, government sectors, and business enterprises
are adopting the cloud environment due to the least upfront capital investment, maximum …

Secure two-party neural network inference (2PC-NN) can offer privacy protection for both the
client and the server and is a promising technique in the machine-learning-as-a-service …

Data privacy concerns are increasing significantly in the context of the Internet of Things,
cloud services, edge computing, artificial intelligence applications, and other applications …

Abstract Machine learning are vulnerable to the threats. The Intruders can utilize the
malicious nature of the nodes to attack the training dataset to worsen the process and …

As the general development trend of the automotive industry, connected and autonomous
vehicles (CAVs) can be used to increase transportation safety, promote mobility choices …

With the recent wave of progress in artificial intelligence (AI) has come a growing awareness
of the large-scale impacts of AI systems, and recognition that existing regulations and norms …

Edge computing is a key-enabling technology that meets continuously increasing
requirements for the intelligent Internet-of-Things (IoT) applications. To cope with the …

Advancements in deep learning enable cloud servers to provide inference-as-a-service for
clients. In this scenario, clients send their raw data to the server to run the deep learning …

In this paper, we address the problem of privacy-preserving training and evaluation of neural
networks in an $ N $-party, federated learning setting. We propose a novel system …