Generating Synthetic Functional Data for Privacy-Preserving GPS Trajectories
Abstract
This research presents FDASynthesis, a novel algorithm designed to generate synthetic GPS trajectory data while preserving privacy. After pre-processing the input GPS data, human mobility traces are modeled as multidimensional curves using Functional Data Analysis (FDA). Then, the synthesis process identifies the K-nearest trajectories and averages their Square-Root Velocity Functions (SRVFs) to generate synthetic data. This results in synthetic trajectories that maintain the utility of the original data while ensuring privacy. Although applied for human mobility research, FDASynthesis is highly adaptable to different types of functional data, offering a scalable solution in various application domains.
Cite
@article{arxiv.2410.12514,
title = {Generating Synthetic Functional Data for Privacy-Preserving GPS Trajectories},
author = {Arianna Burzacchi and Lise Bellanger and Klervi Le Gall and Aymeric Stamm and Simone Vantini},
journal= {arXiv preprint arXiv:2410.12514},
year = {2024}
}
Comments
Updated version, correction of the notation