Blockchain, an emerging paradigm of secure and shareable computing, is a systematic
integration of 1) chain structure for data verification and storage, 2) distributed consensus …

Personal data are often collected and processed in a decentralized fashion, within different
contexts. For instance, with the emergence of distributed applications, several providers are …

Federated learning (FL) is an emerging paradigm that enables multiple organizations to
jointly train a model without revealing their private data to each other. This paper studies {\it …

Abstract Machine learning models trained on sensitive real-world data promise
improvements to everything from medical screening to disease outbreak discovery. In many …

Thanks to the widespread deployment of TLS, users can access private data over channels
with end-to-end confidentiality and integrity. What they cannot do, however, is prove to third …

Sensitive personal data are created in many application domains, and there is now an
increasing demand to share, integrate, and link such data within and across organisations in …

Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-
world tasks on graph data, consisting of node features and the adjacent information between …

Background Record linkage is the process of identifying and combining records about the
same individual from two or more different datasets. While there are many open source and …

We introduce the concept of “coded computing”, a novel computing paradigm that utilizes
coding theory to effectively inject and leverage data/computation redundancy to mitigate …

With the rapid development of IoT technology, millions of physical devices embedded with
electronics or software are put into regular production. Each IoT device is connected to the …