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Spatial Algorithms Syst."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>\n            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\n            <jats:italic>W-Trace<\/jats:italic>\n            - a novel, effective, robust, and utility-preserving GPS trajectory watermarking method.\n            <jats:italic>W-Trace<\/jats:italic>\n            transforms a GPS trajectory into a complex domain and applies the Fourier transformation to decompose the trajectory into the frequency representation.\n            <jats:italic>W-Trace<\/jats:italic>\n            embeds watermarks in the frequency representation and verifies them in a spatiotemporally-aware procedure. We demonstrate the effectiveness, robustness, and utility of the proposed\n            <jats:italic>W-Trace<\/jats:italic>\n            approach in realistic settings using real-world GPS trajectory datasets. In contrast to the baselines, the proposed\n            <jats:italic>W-Trace<\/jats:italic>\n            approach is robust to a wide range of trajectory modifications while preserving the GPS trajectory characteristics required for the downstream applications.\n          <\/jats:p>","DOI":"10.1145\/3701558","type":"journal-article","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T10:04:03Z","timestamp":1729850643000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Towards Effective, Robust and Utility-preserving Watermarking of GPS Trajectories"],"prefix":"10.1145","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0277-1258","authenticated-orcid":false,"given":"Rajjat","family":"Dadwal","sequence":"first","affiliation":[{"name":"Data Science &amp; Intelligent Systems Group (DSIS), University of Bonn, Bonn, Germany and Lamarr Institute for Machine Learning and Artificial Intelligence, Bonn, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6196-8312","authenticated-orcid":false,"given":"Thorben","family":"Funke","sequence":"additional","affiliation":[{"name":"L3S Research Center, Leibniz Universit\u00e4t Hannover, Hannover, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3132-9326","authenticated-orcid":false,"given":"Michael","family":"N\u00fcsken","sequence":"additional","affiliation":[{"name":"Bonn-Aachen International Center for Information Technology, Bonn, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5134-9072","authenticated-orcid":false,"given":"Elena","family":"Demidova","sequence":"additional","affiliation":[{"name":"Data Science &amp; Intelligent Systems Group (DSIS), University of Bonn, Bonn, Germany and Lamarr Institute for Machine Learning and Artificial Intelligence, Bonn, Germany"}]}],"member":"320","published-online":{"date-parts":[[2025,2,14]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1145\/1142473.1142531","volume-title":"Proceedings of the ACM SIGMOD International Conference on Management of Data 2006","author":"Achtert Elke","year":"2006","unstructured":"Elke Achtert, Christian B\u00f6hm, Peer Kr\u00f6ger, Peter Kunath, Alexey Pryakhin, and Matthias Renz. 2006. 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