Generating Synthetic Functional Data for Privacy-Preserving GPS Trajectories

TL;DR

This paper introduces FDASynthesis, an innovative algorithm that generates synthetic GPS trajectories using Functional Data Analysis to preserve privacy and utility, adaptable across different functional data types.

Contribution

The paper presents a novel FDA-based method for creating privacy-preserving synthetic GPS trajectories, enhancing data utility and privacy simultaneously.

Findings

Synthetic trajectories retain key mobility patterns.

The method effectively balances privacy and data utility.

Applicable to various types of functional data.

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.