让第三方图神经网络在云端安全推理，保护数据与模型双方隐私。
CryptGNN: Enabling Secure Inference for Graph Neural Networks
- 用多方安全计算实现安全的消息传递与特征转换。
- 支持任意数量参与方，即使大部分合谋也保证安全。
- 适合需要保护数据和模型的云服务场景。
我们提出CryptGNN，一种针对云端第三方图神经网络（GNN）模型的安全高效推理方案，适用于客户端以机器学习即服务（MLaaS）方式访问模型的场景。CryptGNN的核心创新在于采用分布式安全多方计算（SMPC）技术，实现安全的消息传递与特征变换层。该方案可保护客户端的输入数据与图结构免受云服务商及第三方模型拥有者的窥探，同时保护模型参数不被云服务商及客户端获取。CryptGNN支持任意数量的SMPC参与方，无需可信服务器，且在最多P-1个云中参与方串通的情况下仍具有可证明安全性。理论分析与实证实验均验证了CryptGNN在安全性与效率方面的优越性。
原文摘要 · Abstract (English)
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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