Mapp: A model-agnostic privacy-preserving framework for two-party graph neural network inference
- DOI
- 10.1016/j.neunet.2026.109133
- Published
- 2026-11
- Container
- Neural Networks
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.109133,
title = {Mapp: A model-agnostic privacy-preserving framework for two-party graph neural network inference},
author = {Juxiang Zeng and Pinghui Wang and Yangchao Qian and Tingqin Liu and Jing Tao and Xiaohong Guan},
year = {2026},
journal = {Neural Networks},
doi = {10.1016/j.neunet.2026.109133},
url = {https://doi.org/10.1016/j.neunet.2026.109133}
}RIS
TY - JOUR TI - Mapp: A model-agnostic privacy-preserving framework for two-party graph neural network inference AU - Juxiang Zeng AU - Pinghui Wang AU - Yangchao Qian AU - Tingqin Liu AU - Jing Tao AU - Xiaohong Guan PY - 2026 JO - Neural Networks DO - 10.1016/j.neunet.2026.109133 UR - https://doi.org/10.1016/j.neunet.2026.109133 ER -
APA
Zeng, J., Wang, P., Qian, Y., Liu, T., Tao, J., & Guan, X. (2026). Mapp: A model-agnostic privacy-preserving framework for two-party graph neural network inference. Neural Networks. https://doi.org/10.1016/j.neunet.2026.109133
Source records
- crossref · retrieved 2026-09-26T08:38:47.571Z