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.

  • Veröffentlicht in:
    ACM Transactions on Spatial Algorithms and Systems
  • Typ:
    Article
  • Autoren:
    Dadwal, Rajjat; Funke, Thorben; Nüsken, Michael; Demidova, Elena
  • Jahr:
    2024
  • Source:
    https://dl.acm.org/doi/abs/10.1145/3701558

Informationen zur Zitierung

Dadwal, Rajjat; Funke, Thorben; Nüsken, Michael; Demidova, Elena: Towards effective, robust and utility-preserving watermarking of GPS trajectories, ACM Transactions on Spatial Algorithms and Systems, 2024, https://dl.acm.org/doi/abs/10.1145/3701558, Dadwal.etal.2024a,

Assoziierte Lamarr-ForscherInnen

lamarr institute person demidova elena e1663924269458 - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Prof. Dr. Elena Demidova

Principal Investigator Hybrides ML zum Profil