Large language models (LLMs) have strong capabilities in solving diverse natural language
processing tasks. However, the safety and security issues of LLM systems have become the …

Graphics Processing Units (GPUs) unlock emerging use cases like large language models
and autonomous driving. They process a large amount of sensitive data, where security is of …

Privacy and security challenges in Machine Learning (ML) have become increasingly
severe, along with ML's pervasive development and the recent demonstration of large attack …

Graphics Processing Units (GPU) are increasingly deployed on Cyber-physical Systems
(CPSs), frequently used to perform real-time safety-critical functions, such as object …

This article presents an approach to provide strong assurance of the secure execution of
distributed event-driven applications on shared infrastructures, while relying on a small …

Trusted execution environments (TEEs) have been proposed to protect GPU computation for
machine learning applications operating on sensitive data. However, existing GPU TEE …

As the global data strategy deepens and data elements accelerate integrating and flowing
more rapidly, the demand for data security and privacy protection has become increasingly …

Graph neural networks (GNNs) have emerged as a state-of-the-art approach to model and
draw inferences from large scale graph-structured data in various application settings such …

A wide range of Arm endpoints leverage integrated and discrete GPUs to accelerate
computation. However, Arm GPU security has not been explored by the community. Existing …

Confidential computing is an emerging technique that provides users and third-party
developers with an isolated and transparent execution environment. To support this …