2 citations · 2 across the 9 of their papers we have counts for
6 papers · 1 filter
GoldenRetriever: Non-Interactive Homomorphic Encrypted Retrieval for Privacy-Preserving RAG
Yang Gao, Gang Quan, Scott Piersall +3
Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, but existing pipelines typically operate on plaintext data, raising signifi…
Efficient Privacy-Preserving Sparse Matrix-Vector Multiplication Using Homomorphic Encryption
Yang Gao, Gang Quan, Wujie Wen +3
Sparse matrix-vector multiplication (SpMV) is a fundamental operation in scientific computing, data analysis, and machine learning. When the data being processed are sensitive, pre…
CryptoGen: Secure Transformer Generation with Encrypted KV-Cache Reuse
Hedong Zhang, Neusha Javidnia, Shweta Pardeshi +2
The widespread deployment of cloud-hosted generative models raises a fundamental challenge: enabling efficient autoregressive generation while preserving the privacy of both user p…
FHAIM: Fully Homomorphic AIM For Private Synthetic Data Generation
Mayank Kumar, Qian Lou, Paulo Barreto +2
Data is the lifeblood of AI, yet much of the most valuable data remains locked in silos due to privacy and regulations. As a result, AI remains heavily underutilized in many of the…
DESIGN: Encrypted GNN Inference via Server-Side Input Graph Pruning
Kaixiang Zhao, Joseph Yousry Attalla, Qian Lou +1
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in various graph-based learning tasks. However, enabling privacy-preserving GNNs in encrypted domains, such…
Towards Safe AI Clinicians: A Comprehensive Study on Large Language Model Jailbreaking in Healthcare
Hang Zhang, Qian Lou, Yanshan Wang
Large language models (LLMs) are increasingly utilized in healthcare applications. However, their deployment in clinical practice raises significant safety concerns, including the…