1 citations · 2 across the 6 of their papers we have counts for
6 papers
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…
Learning Polynomial Activation Functions for Deep Neural Networks
Linghao Zhang, Jiawang Nie, Tingting Tang
Activation functions are crucial for deep neural networks. This novel work frames the problem of training neural network with learnable polynomial activation functions as a polynom…
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…
Ethos: Rectifying Language Models in Orthogonal Parameter Space
Lei Gao, Yue Niu, Tingting Tang +2
Language models (LMs) have greatly propelled the research on natural language processing. However, LMs also raise concerns regarding the generation of biased or toxic content and t…
Edge Private Graph Neural Networks with Singular Value Perturbation
Tingting Tang, Yue Niu, Salman Avestimehr +1
Graph neural networks (GNNs) play a key role in learning representations from graph-structured data and are demonstrated to be useful in many applications. However, the GNN trainin…