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Privacy-Preserving Inference in Machine Learning Services Using Trusted
  Execution Environments

Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environments

7 December 2019
Krishnagiri Narra
Zhifeng Lin
Yongqin Wang
Keshav Balasubramaniam
M. Annavaram
    BDLFedML
ArXiv (abs)PDFHTML

Papers citing "Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environments"

11 / 11 papers shown
Title
Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training
Characterization of GPU TEE Overheads in Distributed Data Parallel ML Training
Jonghytun Lee
Yongqin Wang
Rachit Rajat
M. Annavaram
102
0
0
20 Jan 2025
Privacy-Enhancing Technologies for Artificial Intelligence-Enabled
  Systems
Privacy-Enhancing Technologies for Artificial Intelligence-Enabled Systems
L. dÁliberti
Evan Gronberg
Joseph Kovba
SILM
67
2
0
04 Apr 2024
State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey
State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey
Chaoyu Zhang
Shaoyu Li
AILaw
128
4
0
25 Feb 2024
CompactTag: Minimizing Computation Overheads in Actively-Secure MPC for
  Deep Neural Networks
CompactTag: Minimizing Computation Overheads in Actively-Secure MPC for Deep Neural Networks
Yongqin Wang
Pratik Sarkar
Nishat Koti
A. Patra
Murali Annavaram
49
2
0
08 Nov 2023
No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN
  Partition for On-Device ML
No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device ML
Ziqi Zhang
Chen Gong
Yifeng Cai
Yuanyuan Yuan
Bingyan Liu
Ding Li
Yao Guo
Xiangqun Chen
FedML
81
21
0
11 Oct 2023
Privacy Protectability: An Information-theoretical Approach
Privacy Protectability: An Information-theoretical Approach
Siping Shi
Bihai Zhang
Dan Wang
47
1
0
25 May 2023
A Survey on Heterogeneous Federated Learning
A Survey on Heterogeneous Federated Learning
Dashan Gao
Xin Yao
Qian Yang
FedML
99
62
0
10 Oct 2022
DarKnight: An Accelerated Framework for Privacy and Integrity Preserving
  Deep Learning Using Trusted Hardware
DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware
H. Hashemi
Yongqin Wang
M. Annavaram
FedML
64
60
0
30 Jun 2022
SoK: Opportunities for Software-Hardware-Security Codesign for Next
  Generation Secure Computing
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
Privacy in Deep Learning: A Survey
Privacy in Deep Learning: A Survey
Fatemehsadat Mirshghallah
Mohammadkazem Taram
Praneeth Vepakomma
Abhishek Singh
Ramesh Raskar
H. Esmaeilzadeh
FedML
116
139
0
25 Apr 2020
Not All Features Are Equal: Discovering Essential Features for
  Preserving Prediction Privacy
Not All Features Are Equal: Discovering Essential Features for Preserving Prediction Privacy
Fatemehsadat Mireshghallah
Mohammadkazem Taram
A. Jalali
Ahmed T. Elthakeb
Dean Tullsen
H. Esmaeilzadeh
65
12
0
26 Mar 2020
1