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Enabling Privacy-Preserving, Compute- and Data-Intensive Computing using Heterogeneous Trusted Execution Environment
9 April 2019
Jianping Zhu
Rui Hou
Xiaofeng Wang
Wenhao Wang
Jiangfeng Cao
Lutan Zhao
Fengkai Yuan
Peinan Li
Zhongpu Wang
Boyan Zhao
Lixin Zhang
Dan Meng
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Papers citing
"Enabling Privacy-Preserving, Compute- and Data-Intensive Computing using Heterogeneous Trusted Execution Environment"
4 / 4 papers shown
Title
Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations
T. Krauß
Alexandra Dmitrienko
AAML
69
5
0
06 Jun 2023
CrowdGuard: Federated Backdoor Detection in Federated Learning
Phillip Rieger
T. Krauß
Markus Miettinen
Alexandra Dmitrienko
Ahmad-Reza Sadeghi Technical University Darmstadt
AAML
FedML
101
21
0
14 Oct 2022
Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspective
Kha Dinh Duy
Taehyun Noh
Siwon Huh
Hojoon Lee
86
9
0
05 Nov 2021
SESAME: Software defined Enclaves to Secure Inference Accelerators with Multi-tenant Execution
Sarbartha Banerjee
Prakash Ramrakhyani
Shijia Wei
Mohit Tiwari
28
9
0
14 Jul 2020
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