16 citations · 24 across the 5 of their papers we have counts for
5 papers
Blueprint, Bootstrap, and Bridge: A Security Look at NVIDIA GPU Confidential Computing
Zhongshu Gu, Enriquillo Valdez, Salman Ahmed +5
NVIDIA GPU Confidential Computing (GPU-CC) aims to provide secure execution for AI workloads. For end users, enabling GPU-CC is seamless and requires no modifications to existing a…
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…
Separation of Powers in Federated Learning
Pau-Chen Cheng, Kevin Eykholt, Zhongshu Gu +4
Federated Learning (FL) enables collaborative training among mutually distrusting parties. Model updates, rather than training data, are concentrated and fused in a central aggrega…
Reaching Data Confidentiality and Model Accountability on the CalTrain
Zhongshu Gu, Hani Jamjoom, Dong Su +5
Distributed collaborative learning (DCL) paradigms enable building joint machine learning models from distrusting multi-party participants. Data confidentiality is guaranteed by re…
Confidential Inference via Ternary Model Partitioning
Zhongshu Gu, Heqing Huang, Jialong Zhang +5
Today's cloud vendors are competing to provide various offerings to simplify and accelerate AI service deployment. However, cloud users always have concerns about the confidentiali…