Privacy-Preserving Federated Mobility Prediction with Compound Data and Model Perturbation Mechanism  

作　　者：Long Qingyue Wang Huandong Chen Huiming Jin Depeng Zhu Lin Yu Li Li Yong 

机构地区：[1]Beijing National Research Center for Information Science and Technology(BNRist),Department of Electronic Engineering,Tsinghua University,Beijing 100084,China [2]China Mobile Research,Beijing 100032,China

出　　处：《China Communications》2024年第3期160-173,共14页中国通信（英文版）

基　　金：supported in part by the National Key Research and Development Program of China under 2020AAA0106000;the National Natural Science Foundation of China under U20B2060 and U21B2036;supported by a grant from the Guoqiang Institute, Tsinghua University under 2021GQG1005

摘　　要：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 learningbased 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 stateof-the-art mobility prediction method in terms of defensive performance against practical attacks while having comparable mobility prediction performance,demonstrating its effectiveness.

关 键 词：federated learning mobility prediction PRIVACY 

分 类 号：TP309[自动化与计算机技术—计算机系统结构] TP18[自动化与计算机技术—计算机科学与技术]