activity
20192023
most citedPubSub-SGX: Exploiting Trusted Execution Environments for Privacy-Preserving Publish/Subscribe Systems

14 citations · 43 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CR2023★ 11 cited

Towards a Formally Verified Security Monitor for VM-based Confidential Computing

Wojciech Ozga, Guerney D. H. Hunt, Michael V. Le +2

Confidential computing is a key technology for isolating high-assurance applications from the large amounts of untrusted code typical in modern systems. Existing confidential compu…

cs.CR2023

Scalable Attestation of Virtualized Execution Environments in Hybrid- and Multi-Cloud

Wojciech Ozga, Patricia Sagmeister, Tamás Visegrády +1

Existing attestation mechanisms lack scalability and support for heterogeneous virtual execution environments (VEEs), such as virtual machines and containers executed inside or out…

cs.CR2023★ 4 cited

Intel TDX Demystified: A Top-Down Approach

Pau-Chen Cheng, Wojciech Ozga, Enriquillo Valdez +5

Intel Trust Domain Extensions (TDX) is a new architectural extension in the 4th Generation Intel Xeon Scalable Processor that supports confidential computing. TDX allows the deploy…

cs.CR2022★ 2 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.CR2021

WELES: Policy-driven Runtime Integrity Enforcement of Virtual Machines

Wojciech Ozga, Do Le Quoc, Christof Fetzer

Trust is of paramount concern for tenants to deploy their security-sensitive services in the cloud. The integrity of VMs in which these services are deployed needs to be ensured ev…

cs.LG2021★ 3 cited

Perun: Secure Multi-Stakeholder Machine Learning Framework with GPU Support

Wojciech Ozga, Do Le Quoc, Christof Fetzer

Confidential multi-stakeholder machine learning (ML) allows multiple parties to perform collaborative data analytics while not revealing their intellectual property, such as ML sou…