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.
Authors
- Thorben Funke (ORCID: https://orcid.org/0000-0001-6196-8312)
- Michael Nüsken
- Rajjat Dadwal (ORCID: https://orcid.org/0000-0003-0277-1258)
- Elena Demidova (ORCID: https://orcid.org/0000-0002-5134-9072)
Institutions
- 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)
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
- Field-Weighted Citation Impact
- 0.00