LinkGuard: Link Locally Privacy-Preserving Graph Neural Networks with Integrated Denoising and Private Learning-Reference-Cited by-同舟云学术

LinkGuard: Link Locally Privacy-Preserving Graph Neural Networks with Integrated Denoising and Private Learning

Published:2024-05-13 Issue: Volume: Page:593-596
ISSN:
Container-title:Companion Proceedings of the ACM Web Conference 2024
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Author:

Qi Yuxin¹^ORCID,Lin Xi¹^ORCID,Liu Ziyao²^ORCID,Li Gaolei¹^ORCID,Wang Jingyu³^ORCID,Li Jianhua¹^ORCID

Affiliation:

1. Shanghai Jiao Tong University, Shanghai, China

2. Nanyang Technological University, Singapore, Singapore

3. Ocean University of China, Qingdao, China

Funder

National Natural Science Foundation of China: 62202302

National Natural Science Foundation of China: U20B2048

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589335.3651533

Reference14 articles.

1. Eli Chien, Wei-Ning Chen, Chao Pan, Pan Li, Ayfer Ozgur, and Olgica Milenkovic. 2023. Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection. In Thirty-seventh Conference on Neural Information Processing Systems.

2. Ameya Daigavane, Gagan Madan, Aditya Sinha, Abhradeep Guha Thakurta, Gaurav Aggarwal, and Prateek Jain. 2021. Node-level differentially private graph neural networks. arXiv preprint arXiv:2111.15521 (2021).

3. Xinlei He, Jinyuan Jia, Michael Backes, Neil Zhenqiang Gong, and Yang Zhang. 2021. Stealing links from graph neural networks. In 30th USENIX Security Symposium (USENIX Security 21). 2669--2686.

4. Seira Hidano and Takao Murakami. 2022. Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy. arXiv preprint arXiv:2202.10209 (2022).

5. Towards Private Learning on Decentralized Graphs With Local Differential Privacy