As an efficient neural network model for graph data, graph neural networks (GNNs) recently find successful applications for various wireless optimization problems. Given that the inference stage of GNNs can be naturally implemented in a decentralized manner, GNN is a potential enabler for decentralized control/management in the next-generation wireless communications. Privacy leakage, however, may occur due to the information exchanges among neighbors during decentralized inference with GNNs. To deal with this issue, in this paper, we analyze and enhance the privacy of decentralized inference with GNNs in wireless networks. Specifically, we adopt local differential privacy as the metric, and design novel privacy-preserving signals as well as privacy-guaranteed training algorithms to achieve privacy-preserving inference. We also define the SNR-privacy trade-off function to analyze the performance upper bound of decentralized inference with GNNs in wireless networks. To further enhance the communication and computation efficiency, we adopt the over-the-air computation technique and theoretically demonstrate its advantage in privacy preservation. Through extensive simulations on the synthetic graph data, we validate our theoretical analysis, verify the effectiveness of proposed privacy-preserving wireless signaling and privacy-guaranteed training algorithm, and offer some guidance on practical implementation.

翻译：作为一种高效的图数据神经网络模型，图神经网络（GNNs）近期在多种无线优化问题中展现出成功应用。鉴于GNN的推理阶段可天然以去中心化方式实现，GNN有望成为下一代无线通信中分散式控制/管理的使能技术。然而，在通过GNN进行去中心化推理时，邻居节点间的信息交换可能导致隐私泄露。针对此问题，本文分析并增强了无线网络中GNN去中心化推理的隐私保护能力。具体而言，我们采用局部差分隐私作为度量标准，设计新型隐私保护信号及隐私保证训练算法以实现隐私保护推理。同时，定义信噪比-隐私权衡函数来分析无线网络中GNN去中心化推理的性能上限。为进一步提升通信与计算效率，我们采用空中计算技术，并从理论上论证其在隐私保护方面的优势。通过在合成图数据上的大量仿真实验，我们验证了理论分析的正确性，证实了所提隐私保护无线信号及隐私保证训练算法的有效性，并为实际部署提供了指导性建议。