1 citations · 4 across the 19 of their papers we have counts for
10 papers · 1 filter
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 (…
Infrastructure for Valuable, Tradable, and Verifiable Agent Memory
Mengyuan Li, Lei Gao, Haoxuan Xu +5
Every API token you spend is your accumulated wealth; once you can prove its value and the effort behind it, you can resell it. As autonomous agents repeatedly call models and tool…
LRD-MPC: Efficient MPC Inference through Low-rank Decomposition
Tingting Tang, Yongqin Wang, Murali Annavaram
Secure Multi-party Computation (MPC) enables untrusted parties to jointly compute a function without revealing their inputs. Its application to machine learning (ML) has gained sig…
Differentially Private Retrieval-Augmented Generation
Tingting Tang, James Flemings, Yongqin Wang +1
Retrieval-augmented generation (RAG) is a widely used framework for reducing hallucinations in large language models (LLMs) on domain-specific tasks by retrieving relevant document…
DistilLock: Safeguarding LLMs from Unauthorized Knowledge Distillation on the Edge
Asmita Mohanty, Gezheng Kang, Lei Gao +1
Large Language Models (LLMs) have demonstrated strong performance across diverse tasks, but fine-tuning them typically relies on cloud-based, centralized infrastructures. This requ…
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…
CompactTag: Minimizing Computation Overheads in Actively-Secure MPC for Deep Neural Networks
Yongqin Wang, Pratik Sarkar, Nishat Koti +2
Secure Multiparty Computation (MPC) protocols enable secure evaluation of a circuit by several parties, even in the presence of an adversary who maliciously corrupts all but one of…
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…
High-Throughput Secure Multiparty Computation with an Honest Majority in Various Network Settings
Christopher Harth-Kitzerow, Ajith Suresh, Yongqin Wang +3
In this work, we present novel protocols over rings for semi-honest secure three-party computation (3PC) and malicious four-party computation (4PC) with one corruption. While most…