Characterizing and Optimizing End-to-End Systems for Private Inference

Characterizing and Optimizing End-to-End Systems for Private Inference

14 July 2022

Karthik Garimella

Papers citing "Characterizing and Optimizing End-to-End Systems for Private Inference"

9 / 9 papers shown

Title
Flash: A Hybrid Private Inference Protocol for Deep CNNs with High Accuracy and Low Latency on CPU H. Roh Jinsu Yeo Yeongil Ko Gu-Yeon Wei David Brooks Woo-Seok Choi 79 2 0 20 Jan 2025
FastQuery: Communication-efficient Embedding Table Query for Private LLM Inference Chenqi Lin Tianshi Xu Zebin Yang Runsheng Wang Ru Huang Meng Li 31 0 0 25 May 2024
Hyena: Optimizing Homomorphically Encrypted Convolution for Private CNN Inference H. Roh Woo-Seok Choi 45 1 0 21 Nov 2023
HAAC: A Hardware-Software Co-Design to Accelerate Garbled Circuits Jianqiao Mo Jayanth Gopinath Brandon Reagen 19 22 0 23 Nov 2022
zPROBE: Zero Peek Robustness Checks for Federated Learning Zahra Ghodsi Mojan Javaheripi Nojan Sheybani Xinqiao Zhang Ke Huang F. Koushanfar FedML 37 17 0 24 Jun 2022
CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale Karthik Garimella N. Jha Zahra Ghodsi S. Garg Brandon Reagen 29 3 0 04 Nov 2021
F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption (Extended Version) Axel S. Feldmann Nikola Samardzic A. Krastev S. Devadas R. Dreslinski Karim M. El Defrawy Nicholas Genise Chris Peikert Daniel Sánchez 35 251 0 11 Sep 2021
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU Sijun Tan Brian Knott Yuan Tian David J. Wu BDL FedML 57 183 0 22 Apr 2021
CrypTFlow: Secure TensorFlow Inference Nishant Kumar Mayank Rathee Nishanth Chandran Divya Gupta Aseem Rastogi Rahul Sharma 96 235 0 16 Sep 2019