Efficient ML Models for Practical Secure Inference

26 August 2022

Papers citing "Efficient ML Models for Practical Secure Inference"

8 / 8 papers shown

Title
HawkEye: Statically and Accurately Profiling the Communication Cost of Models in Multi-party Learning Wenqiang Ruan Xin Lin Ruisheng Zhou Guopeng Lin Shui Yu Weili Han 37 0 0 16 Feb 2025
EQO: Exploring Ultra-Efficient Private Inference with Winograd-Based Protocol and Quantization Co-Optimization Wenxuan Zeng Tianshi Xu Meng Li Runsheng Wang MQ 30 0 0 15 Apr 2024
CoPriv: Network/Protocol Co-Optimization for Communication-Efficient Private Inference Wenxuan Zeng Meng Li Haichuan Yang Wen-jie Lu Runsheng Wang Ru Huang 21 6 0 03 Nov 2023
HE-MAN -- Homomorphically Encrypted MAchine learning with oNnx models Martin Nocker David Drexel Michael Rader Alessio Montuoro Pascal Schöttle 13 6 0 16 Feb 2023
Winograd Convolution for Deep Neural Networks: Efficient Point Selection Syed Asad Alam Andrew Anderson B. Barabasz David Gregg 56 25 0 25 Jan 2022
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
CrypTFlow: Secure TensorFlow Inference Nishant Kumar Mayank Rathee Nishanth Chandran Divya Gupta Aseem Rastogi Rahul Sharma 91 235 0 16 Sep 2019
Densely Connected Convolutional Networks Gao Huang Zhuang Liu L. V. D. van der Maaten Kilian Q. Weinberger PINN 3DV 247 36,356 0 25 Aug 2016