HEQuant: Marrying Homomorphic Encryption and Quantization for Communication-Efficient Private Inference

29 January 2024

Papers citing "HEQuant: Marrying Homomorphic Encryption and Quantization for Communication-Efficient Private Inference"

6 / 6 papers shown

Title
Toward Practical Privacy-Preserving Convolutional Neural Networks Exploiting Fully Homomorphic Encryption J. Park Donghwan Kim Jongmin Kim Sangpyo Kim Wonkyung Jung Jung Hee Cheon Jung Ho Ahn FedML 27 8 0 25 Oct 2023
Impala: Low-Latency, Communication-Efficient Private Deep Learning Inference Woojin Choi Brandon Reagen Gu-Yeon Wei David Brooks FedML 45 7 0 13 May 2022
A comprehensive review of Binary Neural Network Chunyu Yuan S. Agaian MQ 37 95 0 11 Oct 2021
CrypTFlow2: Practical 2-Party Secure Inference Deevashwer Rathee Mayank Rathee Nishant Kumar Nishanth Chandran Divya Gupta Aseem Rastogi Rahul Sharma 77 301 0 13 Oct 2020
Forward and Backward Information Retention for Accurate Binary Neural Networks Haotong Qin Ruihao Gong Xianglong Liu Mingzhu Shen Ziran Wei F. Yu Jingkuan Song MQ 117 324 0 24 Sep 2019
CrypTFlow: Secure TensorFlow Inference Nishant Kumar Mayank Rathee Nishanth Chandran Divya Gupta Aseem Rastogi Rahul Sharma 91 235 0 16 Sep 2019