FedVAE: Trajectory privacy preserving based on Federated Variational AutoEncoder

Yuchen Jiang1; Ying Wu1; Shiyao Zhang2; James J.Q. Yu3

10.1109/VTC2023-Fall60731.2023.10333794

2023

98th IEEE Vehicular Technology Conference (VTC-Fall)

1090-3038

979-8-3503-2929-2

2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall)

1-7

10-13 Oct. 2023

Hong Kong, Hong Kong

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

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.

Federated learning privacy preserving trajectory generation variational autoencoder

第一

英语

EI ; CPCI-S

Stable Support Plan Program of Shenzhen Natural Science Fund[20220815111111002]

Automation & Control Systems ; Computer Science ; Engineering

Automation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Engineering, Mechanical

WOS:001133762500383

20240115323081

Intelligent systems ; Intelligent vehicle highway systems ; Learning systems ; Location ; Location based services ; Privacy-preserving techniques ; Sensitive data ; Telecommunication services ; Traffic control

Highway Systems:406.1 ; Telecommunication; Radar, Radio and Television:716 ; Telephone Systems and Related Technologies; Line Communications:718 ; Data Processing and Image Processing:723.2 ; Artificial Intelligence:723.4 ; Computer Applications:723.5

IEEE

1.Department of Computer Science and Engineering, Southern University of Science and Technology, China
2.Research Institute for Trustworthy Autonomous Systems, Southern University of Science and Technology, China
3.Department of Computer Science, University of York, United Kingdom

Yuchen Jiang,Ying Wu,Shiyao Zhang,et al. FedVAE: Trajectory privacy preserving based on Federated Variational AutoEncoder[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2023:1-7.