37 citations · 99 across the 14 of their papers we have counts for
14 papers
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…
Flexible Swapping for the Cloud
Milan Pandurov, Lukas Humbel, Dmitry Sepp +11
Memory has become the primary cost driver in cloud data centers. Yet, a significant portion of memory allocated to VMs in public clouds remains unused. To optimize this resource, "…
Accelerating Transfer Learning with Near-Data Computation on Cloud Object Stores
Diana Petrescu, Arsany Guirguis, Do Le Quoc +3
Storage disaggregation underlies today's cloud and is naturally complemented by pushing down some computation to storage, thus mitigating the potential network bottleneck between t…
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…
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…
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…