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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...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer Berlin Heidelberg
2022
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| Materias: | |
| 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 |