18 citations · 21 across the 21 of their papers we have counts for
5 papers · 2 filters
PrivQuant: Communication-Efficient Private Inference with Quantized Network/Protocol Co-Optimization
Tianshi Xu, Shuzhang Zhong, Wenxuan Zeng +2
Private deep neural network (DNN) inference based on secure two-party computation (2PC) enables secure privacy protection for both the server and the client. However, existing secu…
FastQuery: Communication-efficient Embedding Table Query for Private LLM Inference
Chenqi Lin, Tianshi Xu, Zebin Yang +3
With the fast evolution of large language models (LLMs), privacy concerns with user queries arise as they may contain sensitive information. Private inference based on homomorphic…
PrivCirNet: Efficient Private Inference via Block Circulant Transformation
Tianshi Xu, Lemeng Wu, Runsheng Wang +1
Homomorphic encryption (HE)-based deep neural network (DNN) inference protects data and model privacy but suffers from significant computation overhead. We observe transforming the…
EQO: Exploring Ultra-Efficient Private Inference with Winograd-Based Protocol and Quantization Co-Optimization
Wenxuan Zeng, Tianshi Xu, Meng Li +1
Private convolutional neural network (CNN) inference based on secure two-party computation (2PC) suffers from high communication and latency overhead, especially from convolution l…
HEQuant: Marrying Homomorphic Encryption and Quantization for Communication-Efficient Private Inference
Tianshi Xu, Meng Li, Runsheng Wang
Secure two-party computation with homomorphic encryption (HE) protects data privacy with a formal security guarantee but suffers from high communication overhead. While previous wo…