{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T11:04:40Z","timestamp":1770548680537,"version":"3.49.0"},"reference-count":31,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,2,16]],"date-time":"2021-02-16T00:00:00Z","timestamp":1613433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key R \\&amp; D Program of China","award":["2017YFB0203201"],"award-info":[{"award-number":["2017YFB0203201"]}]},{"name":"the Key-Area Research and Development Program of Guangdong Province, China","award":["NO.2020B010164003"],"award-info":[{"award-number":["NO.2020B010164003"]}]},{"name":"the Science and Technology Program of Guangdong Province, China","award":["No.2017A010101039"],"award-info":[{"award-number":["No.2017A010101039"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Publication of trajectory data that contain rich information of vehicles in the dimensions of time and space (location) enables online monitoring and supervision of vehicles in motion and offline traffic analysis for various management tasks. However, it also provides security holes for privacy breaches as exposing individual\u2019s privacy information to public may results in attacks threatening individual\u2019s safety. Therefore, increased attention has been made recently on the privacy protection of trajectory data publishing. However, existing methods, such as generalization via anonymization and suppression via randomization, achieve protection by modifying the original trajectory to form a publishable trajectory, which results in significant data distortion and hence a low data utility. In this work, we propose a trajectory privacy-preserving method called dynamic anonymization with bounded distortion. In our method, individual trajectories in the original trajectory set are mixed in a localized manner to form synthetic trajectory data set with a bounded distortion for publishing, which can protect the privacy of location information associated with individuals in the trajectory data set and ensure a guaranteed utility of the published data both individually and collectively. Through experiments conducted on real trajectory data of Guangzhou City Taxi statistics, we evaluate the performance of our proposed method and compare it with the existing mainstream methods in terms of privacy preservation against attacks and trajectory data utilization. The results show that our proposed method achieves better performance on data utilization than the existing methods using globally static anonymization, without trading off the data security against attacks.<\/jats:p>","DOI":"10.3390\/ijgi10020078","type":"journal-article","created":{"date-parts":[[2021,2,16]],"date-time":"2021-02-16T08:09:09Z","timestamp":1613462949000},"page":"78","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Privacy-Preserving Trajectory Data Publishing by Dynamic Anonymization with Bounded Distortion"],"prefix":"10.3390","volume":"10","author":[{"given":"Songyuan","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science, Sun Yat-sen University, GuangZhou 510275, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Information and Communication Technology, Griffith University, Nathan 4111, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Sun Yat-sen University, GuangZhou 510275, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingpeng","family":"Sang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Sun Yat-sen University, GuangZhou 510275, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1820","DOI":"10.3724\/SP.J.1016.2011.01820","article-title":"A survey of trajectory privacy-preserving techniques","volume":"34","author":"Huo","year":"2011","journal-title":"Jisuanji Xuebao Chin. 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