10 citations · 13 across the 7 of their papers we have counts for
7 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…
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…
JigsawComm: Joint Semantic Feature Encoding and Transmission for Communication-Efficient Cooperative Perception
Chenyi Wang, Zhaowei Li, Ming F. Li +1
Multi-agent cooperative perception (CP) promises to overcome the inherent occlusion and range limitations of single-agent systems in autonomous driving, yet its practicality is sev…
AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing
Tong Zhou, Jiahui Zhao, Yukui Luo +4
Private inference (PI) has emerged as a promising solution to execute computations on encrypted data, safeguarding user privacy and model parameters in edge computing. However, exi…
SSNet: A Lightweight Multi-Party Computation Scheme for Practical Privacy-Preserving Machine Learning Service in the Cloud
Shijin Duan, Chenghong Wang, Hongwu Peng +4
As privacy-preserving becomes a pivotal aspect of deep learning (DL) development, multi-party computation (MPC) has gained prominence for its efficiency and strong security. Howeve…
Zero-Space Cost Fault Tolerance for Transformer-based Language Models on ReRAM
Bingbing Li, Geng Yuan, Zigeng Wang +6
Resistive Random Access Memory (ReRAM) has emerged as a promising platform for deep neural networks (DNNs) due to its support for parallel in-situ matrix-vector multiplication. How…
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…