10 citations · 29 across the 10 of their papers we have counts for
10 papers
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…
LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference
Hongwu Peng, Ran Ran, Yukui Luo +8
The growth of Graph Convolution Network (GCN) model sizes has revolutionized numerous applications, surpassing human performance in areas such as personal healthcare and financial…
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…
RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference
Hongwu Peng, Shanglin Zhou, Yukui Luo +11
The proliferation of deep learning (DL) has led to the emergence of privacy and security concerns. To address these issues, secure Two-party computation (2PC) has been proposed as…
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…
PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference
Hongwu Peng, Shanglin Zhou, Yukui Luo +10
The rapid growth and deployment of deep learning (DL) has witnessed emerging privacy and security concerns. To mitigate these issues, secure multi-party computation (MPC) has been…
EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System
Sahidul Islam, Shanglin Zhou, Ran Ran +4
IoT devices are increasingly being implemented with neural network models to enable smart applications. Energy harvesting (EH) technology that harvests energy from ambient environm…
NeuGuard: Lightweight Neuron-Guided Defense against Membership Inference Attacks
Nuo Xu, Binghui Wang, Ran Ran +2
Membership inference attacks (MIAs) against machine learning models can lead to serious privacy risks for the training dataset used in the model training. In this paper, we propose…