SAP-TrajWGP: A Semantic-Aware Personalized Trajectory Privacy Protection Algorithm Based on WGAN-GP

SAP-TrajWGP: A Semantic-Aware Personalized Trajectory Privacy Protection Algorithm Based on WGAN-GP | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article SAP-TrajWGP: A Semantic-Aware Personalized Trajectory Privacy Protection Algorithm Based on WGAN-GP Yu Qiao, Hao Ji, Guowei Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8837598/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract The widespread adoption of the Internet of Vehicles (IoV) and location-based services (LBS) has generated massive trajectory data, which drives intelligent transportation innovations but also causes severe privacy leakage risks. Existing trajectory privacy protection methods face problems of low-quality synthetic data, insufficient semantic protection, and lack of personalization. To address these issues, this paper proposes a Semantic-Aware Personalized Trajectory Privacy Protection algorithm (SAP-TrajWGP) based on Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP). Firstly, a WGAN-GP model integrating LSTM and self-attention mechanisms is constructed to generate high-authenticity synthetic trajectories with spatiotemporal consistency. Secondly, a Bidirectional Gated Recurrent Unit (Bi-GRU) model dynamically identifies sensitive trajectory segments, and a hierarchical differential privacy (DP) perturbation mechanism is implemented based on user-specific privacy preferences. Experiments on real trajectory datasets show that the proposed algorithm achieves an RMSE of 28 meters, spatial and temporal JSD of 0.17 and 0.21 respectively, a TUL attack success rate of 18.4%, and an MI value of 1.67, outperforming mainstream baseline methods in both data utility and privacy protection. This research provides an effective technical pathway for secure and controllable trajectory data sharing. Physical sciences/Engineering Physical sciences/Mathematics and computing Trajectory Privacy Protection Generative Adversarial Networks Semantic Awareness Differential Privacy Personalized Protection Full Text Additional Declarations No competing interests reported. Supplementary Files DataAvailabilityStatement.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 01 Mar, 2026 Reviewers agreed at journal 28 Feb, 2026 Reviewers invited by journal 24 Feb, 2026 Editor assigned by journal 24 Feb, 2026 Editor invited by journal 24 Feb, 2026 Submission checks completed at journal 12 Feb, 2026 First submitted to journal 12 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8837598","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":596417476,"identity":"2bc84bf9-41f8-4a6d-abdb-6b875cdc7b94","order_by":0,"name":"Yu Qiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACNvbmg4//VEjY8fM3HyBOCx/PsWQDnjMWyZIzjiUQp0VOIkdNgretgnHDgRwDIh0mkcMmIdkmwWxw4MzHG28Y7OR0Gwhp4Xl72MLgnASf5OHezZZzGJKNzQ4Q0sKel3gjoUyCme/A2W3SPAwHErcR1MKQYyBxgE2CseFAzjMitXDkGEk2tEkwTjiQw0akFmAgGzOckQAFsrHlHAMi/CLfDoxKhoo6UFQ+vPGmwk6OoBYUIMFDZNQgayFVxygYBaNgFIwIAADpAkIG9sIesgAAAABJRU5ErkJggg==","orcid":"","institution":"Nanjing Tech University Pujiang Institute","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Qiao","suffix":""},{"id":596417477,"identity":"cad42b04-5001-4116-9dfb-6dd90c66a19d","order_by":1,"name":"Hao Ji","email":"","orcid":"","institution":"Pinduoduo Inc","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Ji","suffix":""},{"id":596417478,"identity":"e3330e04-3435-4275-ba1a-ebdf29d539e6","order_by":2,"name":"Guowei Li","email":"","orcid":"","institution":"Nanjing Tech University Pujiang Institute","correspondingAuthor":false,"prefix":"","firstName":"Guowei","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-02-10 07:09:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8837598/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8837598/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103625158,"identity":"5d6facea-8180-4a82-9042-669a7d142e2e","added_by":"auto","created_at":"2026-02-27 20:00:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":670963,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8837598/v1_covered_f3b0f12d-ccbc-4973-8c11-df440e9027c1.pdf"},{"id":103625157,"identity":"4b8f3d69-1a2a-4839-9f7d-afc7ece47daa","added_by":"auto","created_at":"2026-02-27 20:00:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":86955,"visible":true,"origin":"","legend":"","description":"","filename":"DataAvailabilityStatement.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8837598/v1/d4d7cc694f8c30747c314072.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SAP-TrajWGP: A Semantic-Aware Personalized Trajectory Privacy Protection Algorithm Based on WGAN-GP","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Trajectory Privacy Protection, Generative Adversarial Networks, Semantic Awareness, Differential Privacy, Personalized Protection","lastPublishedDoi":"10.21203/rs.3.rs-8837598/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8837598/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe widespread adoption of the Internet of Vehicles (IoV) and location-based services (LBS) has generated massive trajectory data, which drives intelligent transportation innovations but also causes severe privacy leakage risks. Existing trajectory privacy protection methods face problems of low-quality synthetic data, insufficient semantic protection, and lack of personalization. To address these issues, this paper proposes a Semantic-Aware Personalized Trajectory Privacy Protection algorithm (SAP-TrajWGP) based on Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP). Firstly, a WGAN-GP model integrating LSTM and self-attention mechanisms is constructed to generate high-authenticity synthetic trajectories with spatiotemporal consistency. Secondly, a Bidirectional Gated Recurrent Unit (Bi-GRU) model dynamically identifies sensitive trajectory segments, and a hierarchical differential privacy (DP) perturbation mechanism is implemented based on user-specific privacy preferences. Experiments on real trajectory datasets show that the proposed algorithm achieves an RMSE of 28 meters, spatial and temporal JSD of 0.17 and 0.21 respectively, a TUL attack success rate of 18.4%, and an MI value of 1.67, outperforming mainstream baseline methods in both data utility and privacy protection. 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