Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks

by   Mengyuan Lee, et al.

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.


page 8

page 9

page 10

page 20

page 22

page 24

page 26

page 27


Decentralized Graph Neural Network for Privacy-Preserving Recommendation

Building a graph neural network (GNN)-based recommender system without v...

Decentralized Inference with Graph Neural Networks in Wireless Communication Systems

Graph neural network (GNN) is an efficient neural network model for grap...

Unveiling the Role of Message Passing in Dual-Privacy Preservation on GNNs

Graph Neural Networks (GNNs) are powerful tools for learning representat...

Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service

Graphs are widely used to model the complex relationships among entities...

Decentralized Channel Management in WLANs with Graph Neural Networks

Wireless local area networks (WLANs) manage multiple access points (APs)...

Federated Graph Learning for Low Probability of Detection in Wireless Ad-Hoc Networks

Low probability of detection (LPD) has recently emerged as a means to en...

Graph Representation Learning for Wireless Communications

Wireless networks are inherently graph-structured, which can be utilized...

Please sign up or login with your details

Forgot password? Click here to reset