Researchers Run Privacy-Preserving GNN Inference Across Three Microcontrollers

An independent researcher has been exploring whether Graph Neural Networks (GNNs) can perform inference on traffic data across three microcontrollers without any single device ever accessing plaintext inputs, model weights, or intermediate values. The approach uses Replicated Secret Sharing (RSS) in a three-party semi-honest setting, where each data value is split into shares so no individual party can reconstruct the original. A key insight reduces communication overhead: since road intersection topology is public knowledge, the adjacency matrix multiplication requires zero inter-device communication, cutting total rounds from five or more down to three per two-layer GCN. The target hardware is ESP32-S3 microcontrollers, making the system relevant for resource-constrained, real-world deployments. The work addresses a practical privacy concern in multi-agency traffic coordination, where sharing raw sensor data could expose movement patterns or individual vehicle trajectories.