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Trajectory datasets have spatiotemporal features and are a rich information source. The mining of trajectory data can reveal interesting patterns of human activities and behaviors. However, trajectory data can also be exploited to disclose users\u2019 privacy information, e.g., the places they live and work, which could be abused by a malicious user. Therefore, it is very important to protect the users\u2019 privacy before publishing any trajectory data. While most previous research on this subject has only considered the privacy protection of stay points, this paper distinguishes itself by modeling and processing semantic trajectories, which not only contain spatiotemporal data but also involve POI information and the users\u2019 motion modes such as walking, running, driving, etc. Accordingly, in this research, semantic trajectory anonymizing based on the k-anonymity model is proposed that can form sensitive areas that contain k\u2009\u2212\u20091 POI points that are similar to the sensitive points. Then, trajectory ambiguity is executed based on the motion modes, road network topologies and road weights in the sensitive area. Finally, a similarity comparison is performed to obtain the recordable and releasable anonymity trajectory sets. Experimental results show that this method performs efficiently and provides high privacy levels.<\/jats:p>","DOI":"10.1007\/s11276-019-02058-8","type":"journal-article","created":{"date-parts":[[2019,6,15]],"date-time":"2019-06-15T12:02:48Z","timestamp":1560600168000},"page":"5551-5560","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Privacy preserving semantic trajectory data publishing for mobile location-based services"],"prefix":"10.1007","volume":"26","author":[{"given":"Rong","family":"Tan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Tao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Si","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1592-2468","authenticated-orcid":false,"given":"Yuan-Yuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,6,15]]},"reference":[{"key":"2058_CR1","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.future.2018.04.064","volume":"28","author":"HH Gao","year":"2018","unstructured":"Gao, H. 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