Graph Neural Networks for Privacy-Preserving Recommendation with Secure Hardware - Citegraph

Paper Info

Title
Graph Neural Networks for Privacy-Preserving Recommendation with Secure Hardware

Abstract
Local differential privacy (LDP) is widely used in graph neural networks (GNNs) for recommendation to protect users’ privacy. However, existing LDP-based GNNs usually introduce too much noise caused by the untrusted servers and result in poor model accuracy. The emergence of trusted execution environments such as intel SGX can guarantee code integrity and data confidentiality, and lead a new direc...

Year	DOI	Venue
2021	10.1109/NaNA53684.2021.00075	2021 International Conference on Networking and Network Applications (NaNA)
Keywords	DocType	ISBN
Differential privacy,Privacy,Computational modeling,Side-channel attacks,Lead,Data models,Graph neural networks	Conference	978-1-6654-4158-2
Citations	PageRank	References
0	0.34	0
Authors
6

Authors (6 rows)

Cited by (0 rows)

References (0 rows)

Name	Order	Citations	PageRank
Sisong Ru	1	0	0.34
Bingbing Zhang	2	0	0.34
Yixin Jie	3	0	0.34
Chi Zhang	4	0	0.34
Lingbo Wei	5	0	0.34
Chengjie Gu	6	0	0.34

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