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Spatial Algorithms Syst."],"published-print":{"date-parts":[[2024,12,31]]},"abstract":"<jats:p>\n            GPS trajectories are a critical asset for building spatio-temporal predictive models in urban regions in the context of road safety monitoring, traffic management, and mobility services. Currently, reliable and efficient data misuse detection methods for such personal, spatio-temporal data, particularly in data breach cases, are missing. This article addresses an essential aspect of data misuse detection, namely, the re-identification of leaked and potentially modified GPS trajectories. We present\n            <jats:italic>RE-Trace<\/jats:italic>\n            \u2014a contrastive learning-based model that facilitates reliable and efficient re-identification of GPS trajectories and resists specific trajectory transformation attacks aimed to obscure a trajectory\u2019s origin.\n            <jats:italic>RE-Trace<\/jats:italic>\n            utilizes contrastive learning with a transformer-based trajectory encoder to create trajectory representations, robust to various trajectory modifications. We present a comprehensive threat model for GPS trajectory modifications and demonstrate the effectiveness and efficiency of the\n            <jats:italic>RE-Trace<\/jats:italic>\n            re-identification approach on three real-world datasets. Our evaluation results demonstrate that\n            <jats:italic>RE-Trace<\/jats:italic>\n            significantly outperforms state-of-the-art baselines on all datasets and identifies modified GPS trajectories effectively and efficiently.\n          <\/jats:p>","DOI":"10.1145\/3643680","type":"journal-article","created":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T07:25:06Z","timestamp":1707204306000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["<i>RE-Trace<\/i>\n            : Re-identification of Modified GPS Trajectories"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2745-8009","authenticated-orcid":false,"given":"Stefan","family":"Schestakov","sequence":"first","affiliation":[{"name":"L3S Research Center, Leibniz Universit\u00e4t Hannover, Hannover, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2576-4640","authenticated-orcid":false,"given":"Simon","family":"Gottschalk","sequence":"additional","affiliation":[{"name":"L3S Research Center, Leibniz Universit\u00e4t Hannover, Hannover, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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, Lamarr Institute for Machine Learning and Artificial Intelligence, Bonn, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,10,23]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2016.01.008"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.3238"},{"key":"e_1_3_2_4_2","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1145\/1066157.1066213","volume-title":"Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD\u201905)","author":"Chen Lei","year":"2005","unstructured":"Lei Chen, M. 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