10 citations · 16 across the 5 of their papers we have counts for
5 papers · 1 filter
FEnc: Unifying Data Packing for Efficient Private Inference via Convolution and Architecture-Aware Fragment Encoding
Ran Ran, Zhaoting Gong, Nuo Xu +3
Fully Homomorphic Encryption (FHE) enables privacy-preserving machine learning but incurs extreme computational and memory overhead. These costs come not only from expensive low-le…
AEGIS: Scaling Long-Sequence Homomorphic Encrypted Transformer Inference via Hybrid Parallelism on Multi-GPU Systems
Zhaoting Gong, Ran Ran, Fan Yao +1
Fully Homomorphic Encryption (FHE) enables privacy-preserving Transformer inference, but long-sequence encrypted Transformers quickly exceed single-GPU memory capacity because enco…
FHE-Agent: Automating CKKS Configuration for Practical Encrypted Inference via an LLM-Guided Agentic Framework
Nuo Xu, Zhaoting Gong, Ran Ran +3
Fully Homomorphic Encryption (FHE), particularly the CKKS scheme, is a promising enabler for privacy-preserving MLaaS, but its practical deployment faces a prohibitive barrier: it…
PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deployment
Hongwu Peng, Shanglin Zhou, Yukui Luo +9
Two-party computation (2PC) is promising to enable privacy-preserving deep learning (DL). However, the 2PC-based privacy-preserving DL implementation comes with high comparison pro…
CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference
Ran Ran, Nuo Xu, Wei Wang +3
Recently cloud-based graph convolutional network (GCN) has demonstrated great success and potential in many privacy-sensitive applications such as personal healthcare and financial…