Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private
Inference

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Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference

31 May 2020

Hsien-Hsin S. Lee

David Brooks

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Papers citing "Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference"

7 / 7 papers shown

Title
Hyena: Optimizing Homomorphically Encrypted Convolution for Private CNN Inference H. Roh Woo-Seok Choi 111 1 0 21 Nov 2023
Impala: Low-Latency, Communication-Efficient Private Deep Learning Inference Woojin Choi Brandon Reagen Gu-Yeon Wei David Brooks FedML 85 7 0 13 May 2022
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 126 263 0 11 Sep 2021
SoK: Opportunities for Software-Hardware-Security Codesign for Next Generation Secure Computing Deeksha Dangwal M. Cowan Armin Alaghi Vincent T. Lee Brandon Reagen Caroline Trippel 22 2 0 02 May 2021
Enabling Inference Privacy with Adaptive Noise Injection Sanjay Kariyappa Ousmane Amadou Dia Moinuddin K. Qureshi 56 5 0 06 Apr 2021
Porcupine: A Synthesizing Compiler for Vectorized Homomorphic Encryption M. Cowan Deeksha Dangwal Armin Alaghi Caroline Trippel Vincent T. Lee Brandon Reagen 30 37 0 19 Jan 2021
CryptoNAS: Private Inference on a ReLU Budget Zahra Ghodsi A. Veldanda Brandon Reagen S. Garg 87 87 0 15 Jun 2020