37 citations · 97 across the 6 of their papers we have counts for
6 papers
EnclaveX: End-to-End Confidential AI with CPU/GPU TEEs
Robert Schambach, Quoc Do Le, Sergei Arnautov +1
Large Language Models (LLMs) have rapidly proliferated, driving widespread adoption of AI applications. Most deployments rely on centralized infrastructures such as Microsoft Azure…
secureTF: A Secure TensorFlow Framework
Do Le Quoc, Franz Gregor, Sergei Arnautov +3
Data-driven intelligent applications in modern online services have become ubiquitous. These applications are usually hosted in the untrusted cloud computing infrastructure. This p…
Trust Management as a Service: Enabling Trusted Execution in the Face of Byzantine Stakeholders
Franz Gregor, Wojciech Ozga, Sébastien Vaucher +7
Trust is arguably the most important challenge for critical services both deployed as well as accessed remotely over the network. These systems are exposed to a wide diversity of t…
PubSub-SGX: Exploiting Trusted Execution Environments for Privacy-Preserving Publish/Subscribe Systems
Sergei Arnautov, Andrey Brito, Pascal Felber +9
This paper presents PUBSUB-SGX, a content-based publish-subscribe system that exploits trusted execution environments (TEEs), such as Intel SGX, to guarantee confidentiality and in…
TensorSCONE: A Secure TensorFlow Framework using Intel SGX
Roland Kunkel, Do Le Quoc, Franz Gregor +3
Machine learning has become a critical component of modern data-driven online services. Typically, the training phase of machine learning techniques requires to process large-scale…
Slick: Secure Middleboxes using Shielded Execution
Bohdan Trach, Alfred Krohmer, Sergei Arnautov +3
Cloud computing offers the economies of scale for computational resources with the ease of management, elasticity, and fault tolerance. To take advantage of these benefits, many en…