CryptGNN: Enabling Secure Inference for Graph Neural Networks

Pritam Sen, Yao Ma, Cristian Borcea
9/11/2025
cs.CRcs.LG

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Abstract

We present CryptGNN, a secure and effective inference solution for third-party graph neural network (GNN) models in the cloud, which are accessed by clients as ML as a service (MLaaS). The main novelty of CryptGNN is its secure message passing and feature transformation layers using distributed secure multi-party computation (SMPC) techniques. CryptGNN protects the client's input data and graph structure from the cloud provider and the third-party model owner, and it protects the model parameters from the cloud provider and the clients. CryptGNN works with any number of SMPC parties, does not require a trusted server, and is provably secure even if P-1 out of P parties in the cloud collude. Theoretical analysis and empirical experiments demonstrate the security and efficiency of CryptGNN.

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Cite this paper

@article{sen2025cryptgnn,
  title  = {CryptGNN: Enabling Secure Inference for Graph Neural Networks},
  author = {Pritam Sen and Yao Ma and Cristian Borcea},
  year   = {2025},
  eprint = {2509.09107},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.09107v1}
}

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