Privacy-preserving federated mobility prediction with compound data and model perturbation mechanism

Human mobility prediction is important for many applications. However, training an accurate mobility prediction model requires a large scale of human trajectories, where privacy issues become an important problem. The rising federated learning provides us with a promising solution to this problem, which enables mobile devices to collaboratively learn a shared prediction model while keeping all the training data on the device, decoupling the ability to do machine learning from the need to store the data in the cloud. However, existing federated learning-based methods either do not provide privacy guarantees or have vulnerability in terms of privacy leakage. In this paper, we combine the techniques of data perturbation and model perturbation mechanisms and propose a privacy-preserving mobility prediction algorithm, where we add noise to the transmitted model and the raw data collaboratively to protect user privacy and keep the mobility prediction performance. Extensive experimental results show that our proposed method significantly outperforms the existing state-of-the-art mobility prediction method in terms of defensive performance against practical attacks while having comparable mobility prediction performance, demonstrating its effectiveness.