Achieving Privacy-Preserving and Scalable Graph Neural Network Prediction in Cloud Environments-Reference-Cited by-同舟云学术

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Achieving Privacy-Preserving and Scalable Graph Neural Network Prediction in Cloud Environments

Published:2024-06-09 Issue: Volume:30 Page:4632-4637
ISSN:
Container-title:ICC 2024 - IEEE International Conference on Communications
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Author:

Yuan Yanli¹,Lei Dian¹,Zhang Chuan¹,Liu Ximeng²,Xiong Zehui³,Zhu Liehuang¹

Affiliation:

1. School of Cyberspace Science and Technology, Beijing Institute of Technology,Beijing,China

2. College of Mathematics and Computer Science, Fuzhou University,Fuzhou,China

3. Singapore University of Technology and Design,Information System and Technology Design Pillar,Singapore

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10622104/10622158/10622439.pdf?arnumber=10622439

Reference17 articles.

1. Poisoning GNN-based Recommender Systems with Generative Surrogate-based Attacks

2. MIDLG: Mutual Information based Dual Level GNN for Transaction Fraud Complaint Verification

3. Learning Dynamic Graph for Overtaking Strategy in Autonomous Driving

4. Achieving Efficient and Privacy-Preserving Neural Network Training and Prediction in Cloud Environments

5. Achieving Privacy-Preserving and Verifiable Support Vector Machine Training in the Cloud

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