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TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service

TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service

9 June 2018
Amartya Sanyal
Matt J. Kusner
Adria Gascon
Varun Kanade
    FedML
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Papers citing "TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service"

18 / 18 papers shown
Title
SECO: Secure Inference With Model Splitting Across Multi-Server
  Hierarchy
SECO: Secure Inference With Model Splitting Across Multi-Server Hierarchy
Shuangyi Chen
Ashish Khisti
18
2
0
24 Apr 2024
Linearizing Models for Efficient yet Robust Private Inference
Linearizing Models for Efficient yet Robust Private Inference
Sreetama Sarkar
Souvik Kundu
P. Beerel
AAML
22
0
0
08 Feb 2024
AutoReP: Automatic ReLU Replacement for Fast Private Network Inference
AutoReP: Automatic ReLU Replacement for Fast Private Network Inference
Hongwu Peng
Shaoyi Huang
Tong Zhou
Yukui Luo
Chenghong Wang
...
Tony Geng
Kaleel Mahmood
Wujie Wen
Xiaolin Xu
Caiwen Ding
OffRL
47
38
0
20 Aug 2023
PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference
Hongwu Peng
Shangli Zhou
Yukui Luo
Shijin Duan
Nuo Xu
...
Tong Geng
Ang Li
Wujie Wen
Xiaolin Xu
Caiwen Ding
44
3
0
20 Sep 2022
MPC-Friendly Commitments for Publicly Verifiable Covert Security
MPC-Friendly Commitments for Publicly Verifiable Covert Security
Nitin Agrawal
James Bell
Adria Gascon
Matt J. Kusner
28
4
0
15 Sep 2021
Precise Approximation of Convolutional Neural Networks for
  Homomorphically Encrypted Data
Precise Approximation of Convolutional Neural Networks for Homomorphically Encrypted Data
Junghyun Lee
Eunsang Lee
Joon-Woo Lee
Yongjune Kim
Young-Sik Kim
Jong-Seon No
24
56
0
23 May 2021
Efficient Encrypted Inference on Ensembles of Decision Trees
Efficient Encrypted Inference on Ensembles of Decision Trees
Kanthi Kiran Sarpatwar
Karthik Nandakumar
Nalini Ratha
J. Rayfield
Karthikeyan Shanmugam
Sharath Pankanti
Roman Vaculin
FedML
22
5
0
05 Mar 2021
Exploring Design and Governance Challenges in the Development of
  Privacy-Preserving Computation
Exploring Design and Governance Challenges in the Development of Privacy-Preserving Computation
Nitin Agrawal
Reuben Binns
Max Van Kleek
Kim Laine
N. Shadbolt
26
43
0
20 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
Secure Evaluation of Quantized Neural Networks
Secure Evaluation of Quantized Neural Networks
Anders Dalskov
Daniel E. Escudero
Marcel Keller
17
138
0
28 Oct 2019
QUOTIENT: Two-Party Secure Neural Network Training and Prediction
QUOTIENT: Two-Party Secure Neural Network Training and Prediction
Nitin Agrawal
Ali Shahin Shamsabadi
Matt J. Kusner
Adria Gascon
30
212
0
08 Jul 2019
BAYHENN: Combining Bayesian Deep Learning and Homomorphic Encryption for
  Secure DNN Inference
BAYHENN: Combining Bayesian Deep Learning and Homomorphic Encryption for Secure DNN Inference
Peichen Xie
Bingzhe Wu
Guangyu Sun
BDL
FedML
19
33
0
03 Jun 2019
SHE: A Fast and Accurate Deep Neural Network for Encrypted Data
SHE: A Fast and Accurate Deep Neural Network for Encrypted Data
Qian Lou
Lei Jiang
21
120
0
01 Jun 2019
SEALion: a Framework for Neural Network Inference on Encrypted Data
SEALion: a Framework for Neural Network Inference on Encrypted Data
Tim van Elsloo
Giorgio Patrini
Hamish Ivey-Law
FedML
30
42
0
29 Apr 2019
XONN: XNOR-based Oblivious Deep Neural Network Inference
XONN: XNOR-based Oblivious Deep Neural Network Inference
M. Riazi
Mohammad Samragh
Hao Chen
Kim Laine
Kristin E. Lauter
F. Koushanfar
FedML
GNN
BDL
25
280
0
19 Feb 2019
MOBIUS: Model-Oblivious Binarized Neural Networks
MOBIUS: Model-Oblivious Binarized Neural Networks
Hiromasa Kitai
Jason Paul Cruz
Naoto Yanai
Naohisa Nishida
Tatsumi Oba
Yuji Unagami
Tadanori Teruya
Nuttapong Attrapadung
Takahiro Matsuda
Goichiro Hanaoka
24
7
0
29 Nov 2018
Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted
  Inference
Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference
Edward Chou
Josh Beal
Daniel Levy
Serena Yeung
Albert Haque
Li Fei-Fei
32
198
0
25 Nov 2018
nGraph-HE: A Graph Compiler for Deep Learning on Homomorphically
  Encrypted Data
nGraph-HE: A Graph Compiler for Deep Learning on Homomorphically Encrypted Data
Fabian Boemer
Yixing Lao
Rosario Cammarota
Casimir Wierzynski
FedML
19
163
0
23 Oct 2018
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