activity
20182025
most citedsecureTF: A Secure TensorFlow Framework

37 citations · 90 across the 9 of their papers we have counts for

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Showing cs.CRShow all

8 papers · 1 filter

cs.CR2025

TICAL: Trusted and Integrity-protected Compilation of AppLications

Robert Krahn, Nikson Kanti Paul, Franz Gregor +4

During the past few years, we have witnessed various efforts to provide confidentiality and integrity for applications running in untrusted environments such as public clouds. In m…

cs.CR20222 cited

Synergia: Hardening High-Assurance Security Systems with Confidential and Trusted Computing

Wojciech Ozga, Rasha Faqeh, Do Le Quoc +3

High-assurance security systems require strong isolation from the untrusted world to protect the security-sensitive or privacy-sensitive data they process. Existing regulations imp…

cs.CR20214 cited

SecFL: Confidential Federated Learning using TEEs

Do Le Quoc, Christof Fetzer

Federated Learning (FL) is an emerging machine learning paradigm that enables multiple clients to jointly train a model to take benefits from diverse datasets from the clients with…

cs.CR202137 cited

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…

cs.CR20217 cited

A practical approach for updating an integrity-enforced operating system

Wojciech Ozga, Do Le Quoc, Christof Fetzer

Trusted computing defines how to securely measure, store, and verify the integrity of software controlling a computer. One of the major challenges that make them hard to be applied…

cs.CR20203 cited

TEEMon: A continuous performance monitoring framework for TEEs

Robert Krahn, Donald Dragoti, Franz Gregor +6

Trusted Execution Environments (TEEs), such as Intel Software Guard eXtensions (SGX), are considered as a promising approach to resolve security challenges in clouds. TEEs protect…