{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T13:50:09Z","timestamp":1775310609527,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Plan Project of Changzhou","award":["CJ20210155"],"award-info":[{"award-number":["CJ20210155"]}]},{"name":"Science and Technology Plan Project of Changzhou","award":["CJ20220151"],"award-info":[{"award-number":["CJ20220151"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Trajectory data contain numerous sensitive attributes. Unauthorized disclosure of precise user trajectory information generates persistent privacy and security concerns that significantly impact daily life. Most existing trajectory privacy protection schemes focus on geographic trajectories while neglecting the critical importance of semantic trajectories, resulting in ongoing privacy vulnerabilities. To address this limitation, we propose the Semantic Behavior Sequence-based Trajectory Privacy Protection method (SBS-TPP). Our approach integrates short-term and long-term behavioral patterns within a user behavior modeling layer to identify user preferences. A dual-model framework (geographic and semantic) generates noise-injected trajectories with maximized noise potential. This methodology applies symmetric noise addition to both geographic trajectory fragments and semantic trajectory segments, optimizing trajectory data utility while ensuring robust protection of sensitive information. The SBS-TPP framework operates in the following two phases: firstly, behavior modeling, which comprises interest extraction from behavioral trajectory sequences, and secondly, noise generation, which creates synthetic noise locations with maximal semantic expectation from original locations, yielding privacy-enhanced trajectories for publication. Experimental results demonstrate that our interest extraction model achieves 93.7% accuracy while maintaining 81.6% data utility under strict privacy guarantees. The proposed method significantly enhances data usability and enables effective recommendation services without compromising user privacy or security.<\/jats:p>","DOI":"10.3390\/sym17081266","type":"journal-article","created":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T08:09:35Z","timestamp":1754640575000},"page":"1266","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Semantic Behavioral Sequence-Based Approach to Trajectory Privacy Protection"],"prefix":"10.3390","volume":"17","author":[{"given":"Ji","family":"Xi","sequence":"first","affiliation":[{"name":"School of Computer Information Engineering, Changzhou Institute of Technology, No.666, Liaohe Road, Changzhou 213022, China"}]},{"given":"Weiqi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Information Engineering, Changzhou Institute of Technology, No.666, Liaohe Road, Changzhou 213022, China"}]},{"given":"Zhengwang","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Computer Information Engineering, Changzhou Institute of Technology, No.666, Liaohe Road, Changzhou 213022, China"}]},{"given":"Li","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Southeast University, Nanjing 210096, China"}]},{"given":"Huawei","family":"Tao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Food Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"130785","DOI":"10.1016\/j.neucom.2025.130785","article-title":"UdpTrace: Utility-enhanced differential privacy scheme for trajectory data publishing","volume":"649","author":"Sun","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cong, X., Zhu, H., Cui, W., Zhao, G., and Yu, Z. 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