FedVAE: Trajectory privacy preserving based on Federated Variational AutoEncoder

TL;DR

FedVAE is a novel federated learning approach utilizing Variational AutoEncoders to generate privacy-preserving trajectory data, maintaining data utility while protecting user confidentiality in location-based services.

Contribution

This paper introduces FedVAE, combining federated learning and VAE to generate realistic, privacy-preserving trajectory data without compromising feature structure or utility.

Findings

FedVAE outperforms existing privacy methods in preserving data utility.

It effectively maintains trajectory data structure while ensuring privacy.

Experimental results show improved privacy and utility balance.

Abstract

The use of trajectory data with abundant spatial-temporal information is pivotal in Intelligent Transport Systems (ITS) and various traffic system tasks. Location-Based Services (LBS) capitalize on this trajectory data to offer users personalized services tailored to their location information. However, this trajectory data contains sensitive information about users' movement patterns and habits, necessitating confidentiality and protection from unknown collectors. To address this challenge, privacy-preserving methods like K-anonymity and Differential Privacy have been proposed to safeguard private information in the dataset. Despite their effectiveness, these methods can impact the original features by introducing perturbations or generating unrealistic trajectory data, leading to suboptimal performance in downstream tasks. To overcome these limitations, we propose a Federated Variational AutoEncoder (FedVAE) approach, which effectively generates a new trajectory dataset while preserving the confidentiality of private information and retaining the structure of the original features. In addition, FedVAE leverages Variational AutoEncoder (VAE) to maintain the original feature space and generate new trajectory data, and incorporates Federated Learning (FL) during the training stage, ensuring that users' data remains locally stored to protect their personal information. The results demonstrate its superior performance compared to other existing methods, affirming FedVAE as a promising solution for enhancing data privacy and utility in location-based applications.