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nGraph-HE2: A High-Throughput Framework for Neural Network Inference on
  Encrypted Data

nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data

12 August 2019
Fabian Boemer
Anamaria Costache
Rosario Cammarota
Casimir Wierzynski
    GNN
ArXivPDFHTML

Papers citing "nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data"

13 / 13 papers shown
Title
Flash: A Hybrid Private Inference Protocol for Deep CNNs with High Accuracy and Low Latency on CPU
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
89
2
0
20 Jan 2025
Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic
  Encryption
Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption
Moran Baruch
Nir Drucker
Gilad Ezov
Yoav Goldberg
Eyal Kushnir
Jenny Lerner
Omri Soceanu
Itamar Zimerman
49
6
0
26 Apr 2023
RPU: The Ring Processing Unit
RPU: The Ring Processing Unit
Deepraj Soni
Negar Neda
Naifeng Zhang
Benedict Reynwar
Homer Gamil
...
David Cousins
F. Franchetti
M. French
A. Schmidt
Brandon Reagen
29
12
0
30 Mar 2023
Towards Automated Homomorphic Encryption Parameter Selection with Fuzzy
  Logic and Linear Programming
Towards Automated Homomorphic Encryption Parameter Selection with Fuzzy Logic and Linear Programming
José Cabrero-Holgueras
S. Pastrana
21
7
0
17 Feb 2023
Privacy-Preserving Credit Card Fraud Detection using Homomorphic
  Encryption
Privacy-Preserving Credit Card Fraud Detection using Homomorphic Encryption
David Nugent
16
2
0
12 Nov 2022
SEEK: model extraction attack against hybrid secure inference protocols
SEEK: model extraction attack against hybrid secure inference protocols
Si-Quan Chen
Junfeng Fan
MIACV
16
2
0
14 Sep 2022
A methodology for training homomorphicencryption friendly neural
  networks
A methodology for training homomorphicencryption friendly neural networks
Moran Baruch
Nir Drucker
L. Greenberg
Guy Moshkowich
23
14
0
05 Nov 2021
Privacy-Preserving Machine Learning with Fully Homomorphic Encryption
  for Deep Neural Network
Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network
Joon-Woo Lee
Hyungchul Kang
Yongwoo Lee
W. Choi
Jieun Eom
...
Eunsang Lee
Junghyun Lee
Donghoon Yoo
Young-Sik Kim
Jong-Seon No
17
245
0
14 Jun 2021
SIRNN: A Math Library for Secure RNN Inference
SIRNN: A Math Library for Secure RNN Inference
Deevashwer Rathee
Mayank Rathee
R. Goli
Divya Gupta
Rahul Sharma
Nishanth Chandran
Aseem Rastogi
24
103
0
10 May 2021
SoK: Fully Homomorphic Encryption Compilers
SoK: Fully Homomorphic Encryption Compilers
Alexander Viand
Patrick Jattke
Anwar Hithnawi
44
98
0
18 Jan 2021
Highly Accurate CNN Inference Using Approximate Activation Functions
  over Homomorphic Encryption
Highly Accurate CNN Inference Using Approximate Activation Functions over Homomorphic Encryption
Takumi Ishiyama
Takuya Suzuki
Hayato Yamana
29
37
0
08 Sep 2020
POSEIDON: Privacy-Preserving Federated Neural Network Learning
POSEIDON: Privacy-Preserving Federated Neural Network Learning
Sinem Sav
Apostolos Pyrgelis
J. Troncoso-Pastoriza
D. Froelicher
Jean-Philippe Bossuat
João Sá Sousa
Jean-Pierre Hubaux
FedML
19
153
0
01 Sep 2020
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function
  Secret Sharing
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing
T. Ryffel
Pierre Tholoniat
D. Pointcheval
Francis R. Bach
FedML
28
94
0
08 Jun 2020
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