3 papers
cs.CR2026
OTRO: Oblivious Tokenization Path with Square-Root ORAM
Jonghyun Lee, Yongqin Wang, Rachit Rajat +3
The CPU-side large language model (LLM) tokenizer is a critical security gap in LLM serving through a confidential computing stack with CPU and GPU trusted execution environments (…
cs.CR2025
Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training
Jonghyun Lee, Yongqin Wang, Rachit Rajat +1
Confidential computing (CC) or trusted execution enclaves (TEEs) is now the most common approach to enable secure computing in the cloud. The recent introduction of GPU TEEs by NVI…
cs.CR2024
Fastrack: Fast IO for Secure ML using GPU TEEs
Yongqin Wang, Rachit Rajat, Jonghyun Lee +2
As cloud-based ML expands, ensuring data security during training and inference is critical. GPU-based Trusted Execution Environments (TEEs) offer secure, high-performance solution…