Defense against membership inference attack in graph neural networks through graph perturbation

Graph neural networks have demonstrated remarkable performance in learning node or graph representations for various graph-related tasks. However, learning with graph data or its embedded representations may induce privacy issues when the node representations contain sensitive or private user inform...

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Detalles Bibliográficos
Autores principales: Wang, Kai, Wu, Jinxia, Zhu, Tianqing, Ren, Wei, Hong, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Regular Contribution
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9756746/
https://www.ncbi.nlm.nih.gov/pubmed/36540905
http://dx.doi.org/10.1007/s10207-022-00646-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9756746/
https://www.ncbi.nlm.nih.gov/pubmed/36540905
http://dx.doi.org/10.1007/s10207-022-00646-y

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