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Enabling Privacy-Preserving, Compute- and Data-Intensive Computing using
  Heterogeneous Trusted Execution Environment
v1v2 (latest)

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
ArXiv (abs)PDFHTML

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
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
CrowdGuard: Federated Backdoor Detection in Federated Learning
Phillip Rieger
T. Krauß
Markus Miettinen
Alexandra Dmitrienko
Ahmad-Reza Sadeghi Technical University Darmstadt
AAMLFedML
101
21
0
14 Oct 2022
Confidential Machine Learning Computation in Untrusted Environments: A
  Systems Security Perspective
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
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
1