Enhancing Trajectory Recovery From Gradients via Mobility Prior Knowledge

As trajectory plays an important role in intelligent transportation systems, achieving trajectory recovery from gradients is of great value. Existing research has shown that federated learning is vulnerable to attacks that recover the original training data from shared gradients. Still, we find that gradients attack is difficult to succeed with trajectory data. Most existing attacks have minimal effectiveness when facing models with higher nonlinearity and temporal-related characteristics, such as destination prediction models. In this article, we propose deep leakage from gradients with mobility prior (DLGMP) algorithm to solve these problems. The proposed DLGMP algorithm leverages the spatiotemporal structural information as the prior mobility knowledge, narrowing down the initial search space sharply and decreasing the difficulty of recovery. We further improve our algorithm with an easily extensible regularization term and an adversarial loss of Wasserstein GAN (WGAN) to help recover accurate and reasonable trajectories. Experiments on three mainstream data sets show good performance of our DLGMP algorithm. We also discuss several possible solutions to prevent gradients attack.

Automated summary

This article proposes a DLGMP algorithm based on deep leakage from gradients and mobility prior knowledge to solve the problem of trajectory data attacks. The algorithm utilizes spatiotemporal structural information as prior mobility knowledge, greatly reducing the difficulty of recovery, and improves the accuracy and reasonableness of trajectory recovery by adding an easily extensible regularization term and an adversarial loss of Wasserstein GAN.