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Toward Practical Privacy-Preserving Convolutional Neural Networks
  Exploiting Fully Homomorphic Encryption

Toward Practical Privacy-Preserving Convolutional Neural Networks Exploiting Fully Homomorphic Encryption

25 October 2023
J. Park
Donghwan Kim
Jongmin Kim
Sangpyo Kim
Wonkyung Jung
Jung Hee Cheon
Jung Ho Ahn
    FedML
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Papers citing "Toward Practical Privacy-Preserving Convolutional Neural Networks Exploiting Fully Homomorphic Encryption"

4 / 4 papers shown
Title
Cheddar: A Swift Fully Homomorphic Encryption Library for CUDA GPUs
Cheddar: A Swift Fully Homomorphic Encryption Library for CUDA GPUs
Jongmin Kim
Wonseok Choi
Jung Ho Ahn
FedML
33
6
0
17 Jul 2024
HEQuant: Marrying Homomorphic Encryption and Quantization for
  Communication-Efficient Private Inference
HEQuant: Marrying Homomorphic Encryption and Quantization for Communication-Efficient Private Inference
Tianshi Xu
Meng Li
Runsheng Wang
29
0
0
29 Jan 2024
F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption
  (Extended Version)
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
CrypTFlow2: Practical 2-Party Secure Inference
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
1