Towards Effective, Robust and Utility-preserving Watermarking of GPS Trajectories

Personal GPS trajectory is essential for businesses and emerging data markets due to its relevance in various data-driven methods, including traffic forecasting, accident prediction, and profiling driving behavior. Watermarking is a method that facilitates verification of data ownership and authenticity by embedding provenance information into the data. Whereas watermarking is commonly adopted in the image and audio domains, only a few initial watermarking methods exist for GPS trajectory data. GPS trajectory watermarking is particularly challenging due to the spatio-temporal data properties and easiness of data modification. As a result, existing watermarking methods often embed only minimal provenance information, lack robustness, and can fail to preserve data utility for downstream applications. In this work, we propose W-Trace - a novel, effective, robust, and utility-preserving GPS trajectory watermarking method. W-Trace transforms a GPS trajectory into a complex domain and applies the Fourier transformation to decompose the trajectory into the frequency representation. W-Trace embeds watermarks in the frequency representation and verifies them in a spatiotemporally-aware procedure. We demonstrate the effectiveness, robustness, and utility of the proposed W-Trace approach in realistic settings using real-world GPS trajectory datasets. In contrast to the baselines, the proposed W-Trace approach is robust to a wide range of trajectory modifications while preserving the GPS trajectory characteristics required for the downstream applications.

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Publication Details

Journal
ACM Transactions on Spatial Algorithms and Systems
Published
2024-10-25
DOI
https://doi.org/10.1145/3701558
Primary Topic
Advanced Steganography and Watermarking Techniques
Type
article
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article

Towards Effective, Robust and Utility-preserving Watermarking of GPS Trajectories

Thorben Funke, Michael Nüsken, Rajjat Dadwal, Elena Demidova
ACM Transactions on Spatial Algorithms and Systems
Advanced Steganography and Watermarking Techniques
article

Towards Effective, Robust and Utility-preserving Watermarking of GPS Trajectories

Thorben Funke, Michael Nüsken, Rajjat Dadwal, Elena Demidova
article en

Abstract

Personal GPS trajectory is essential for businesses and emerging data markets due to its relevance in various data-driven methods, including traffic forecasting, accident prediction, and profiling driving behavior. Watermarking is a method that facilitates verification of data ownership and authenticity by embedding provenance information into the data. Whereas watermarking is commonly adopted in the image and audio domains, only a few initial watermarking methods exist for GPS trajectory data. GPS trajectory watermarking is particularly challenging due to the spatio-temporal data properties and easiness of data modification. As a result, existing watermarking methods often embed only minimal provenance information, lack robustness, and can fail to preserve data utility for downstream applications. In this work, we propose W-Trace - a novel, effective, robust, and utility-preserving GPS trajectory watermarking method. W-Trace transforms a GPS trajectory into a complex domain and applies the Fourier transformation to decompose the trajectory into the frequency representation. W-Trace embeds watermarks in the frequency representation and verifies them in a spatiotemporally-aware procedure. We demonstrate the effectiveness, robustness, and utility of the proposed W-Trace approach in realistic settings using real-world GPS trajectory datasets. In contrast to the baselines, the proposed W-Trace approach is robust to a wide range of trajectory modifications while preserving the GPS trajectory characteristics required for the downstream applications.

ACM Transactions on Spatial Algorithms and SystemsVol. 11(1)
Leibniz University Hannover (DE), University of Bonn (DE), L3S Research Center (DE), Bonn Aachen International Center for Information Technology (DE), Lamarr Institute for Machine Learning and Artificial Intelligence, Fraunhofer-Gesellschaft (DE)
Openalex Percentile: Top 82%
Advanced Steganography and Watermarking Techniques
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