2 citations · 3 across the 5 of their papers we have counts for
5 papers
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 (…
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…
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…
MPC-Pipe: an Efficient Pipeline Scheme for Secure Multi-party Machine Learning Inference
Yongqin Wang, Rachit Rajat, Murali Annavaram
Multi-party computing (MPC) has been gaining popularity as a secure computing model over the past few years. However, prior works have demonstrated that MPC protocols still pay sub…
LAORAM: A Look Ahead ORAM Architecture for Training Large Embedding Tables
Rachit Rajat, Yongqin Wang, Murali Annavaram
Data confidentiality is becoming a significant concern, especially in the cloud computing era. Memory access patterns have been demonstrated to leak critical information such as se…