37 citations · 134 across the 16 of their papers we have counts for
8 papers · 1 filter
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…
Transient Execution of Non-Canonical Accesses
Saidgani Musaev, Christof Fetzer
Recent years have brought microarchitectural security intothe spotlight, proving that modern CPUs are vulnerable toseveral classes of microarchitectural attacks. These attacksbypas…
A Datalog Hammer for Supervisor Verification Conditions Modulo Simple Linear Arithmetic
Martin Bromberger, Irina Dragoste, Rasha Faqeh +3
The Bernays-Schönfinkel first-order logic fragment over simple linear real arithmetic constraints BS(SLR) is known to be decidable. We prove that BS(SLR) clause sets with both univ…
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…
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…
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…