{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:27:43Z","timestamp":1777696063996,"version":"3.51.4"},"reference-count":29,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2021,9,15]]},"abstract":"<jats:p>Trajectory data may include the user\u2019s occupation, medical records, and other similar information. However, attackers can use specific background knowledge to analyze published trajectory data and access a user\u2019s private information. Different users have different requirements regarding the anonymity of sensitive information. To satisfy personalized privacy protection requirements and minimize data loss, we propose a novel trajectory privacy preservation method based on sensitive attribute generalization and trajectory perturbation. The proposed method can prevent an attacker who has a large amount of background knowledge and has exchanged information with other attackers from stealing private user information. First, a trajectory dataset is clustered and frequent patterns are mined according to the clustering results. Thereafter, the sensitive attributes found within the frequent patterns are generalized according to the user requirements. Finally, the trajectory locations are perturbed to achieve trajectory privacy protection. The results of theoretical analyses and experimental evaluations demonstrate the effectiveness of the proposed method in preserving personalized privacy in published trajectory data.<\/jats:p>","DOI":"10.3233\/ida-205306","type":"journal-article","created":{"date-parts":[[2021,9,17]],"date-time":"2021-09-17T11:59:02Z","timestamp":1631879942000},"page":"1247-1271","source":"Crossref","is-referenced-by-count":4,"title":["Personalized trajectory privacy-preserving method based on sensitive attribute generalization and location perturbation"],"prefix":"10.1177","volume":"25","author":[{"given":"Chuanming","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer and Information, Anhui Normal University, Wuhu, Anhui, China"},{"name":"Anhui Provincial Key Laboratory of Network and Information Security, Wuhu, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenshi","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Anhui Normal University, Wuhu, Anhui, China"},{"name":"Anhui Provincial Key Laboratory of Network and Information Security, Wuhu, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuanggui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Anhui Normal University, Wuhu, Anhui, China"},{"name":"Anhui Provincial Key Laboratory of Network and Information Security, Wuhu, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zitong","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Anhui Normal University, Wuhu, Anhui, China"},{"name":"Anhui Provincial Key Laboratory of Network and Information Security, Wuhu, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingying","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Anhui Normal University, Wuhu, Anhui, China"},{"name":"Anhui Provincial Key Laboratory of Network and Information Security, Wuhu, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonglong","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Anhui Normal University, Wuhu, Anhui, China"},{"name":"Anhui Provincial Key Laboratory of Network and Information Security, Wuhu, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-205306_ref1","doi-asserted-by":"crossref","unstructured":"G. Poulis, G. Loukides and A. Gkoulalas-divanis, Anonymizing data with relational and transaction attributes, in: Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2013, pp.\u00a0353\u2013369.","DOI":"10.1007\/978-3-642-40994-3_23"},{"key":"10.3233\/IDA-205306_ref2","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.knosys.2015.11.007","article-title":"PPTD: preserving personalized privacy in trajectory data publishing by sensitive attribute generalization and trajectory local suppression","volume":"94","author":"Ghasemi Komishani","year":"2016","journal-title":"Knowledge-Based Systems"},{"key":"10.3233\/IDA-205306_ref3","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.cageo.2017.12.003","article-title":"An improved optimum-path forest clustering algorithm for remote sensing image segmentation","volume":"112","author":"Chen","year":"2018","journal-title":"Computers & Geosciences"},{"issue":"1","key":"10.3233\/IDA-205306_ref4","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s10462-016-9477-7","article-title":"A review of moving object trajectory clustering algorithms","volume":"47","author":"Yuan","year":"2017","journal-title":"Artificial Intelligence Review"},{"issue":"5","key":"10.3233\/IDA-205306_ref5","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1109\/TKDE.2015.2503753","article-title":"Robust ensemble clustering using probability trajectories","volume":"28","author":"Huang","year":"2015","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"21","key":"10.3233\/IDA-205306_ref6","doi-asserted-by":"crossref","first-page":"7573","DOI":"10.1016\/j.eswa.2015.06.014","article-title":"A general methodology for n-dimensional trajectory clustering","volume":"42","author":"Bermingham","year":"2015","journal-title":"Expert Systems with Applications"},{"issue":"12","key":"10.3233\/IDA-205306_ref7","doi-asserted-by":"crossref","first-page":"207","DOI":"10.3390\/a11120207","article-title":"Trajectory clustering and k-NN for robust privacy preserving spatiotemporal databases","volume":"11","author":"Dritsas","year":"2018","journal-title":"Algorithms"},{"key":"10.3233\/IDA-205306_ref8","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.aap.2019.05.017","article-title":"Key feature selection and risk prediction for lane-changing behaviors based on vehicles\u2019 trajectory data","volume":"129","author":"Chen","year":"2019","journal-title":"Accident Analysis & Prevention"},{"issue":"4","key":"10.3233\/IDA-205306_ref9","first-page":"1645","article-title":"A similar sub-trajectory-based algorithm for moving object trajectory clustering","volume":"15","author":"Chen","year":"2012","journal-title":"Information"},{"issue":"5","key":"10.3233\/IDA-205306_ref10","doi-asserted-by":"crossref","first-page":"164","DOI":"10.3390\/ijgi7050164","article-title":"A trajectory regression clustering technique combining a novel fuzzy C-means clustering algorithm with the least squares method","volume":"7","author":"Zhou","year":"2018","journal-title":"ISPRS International Journal of Geo-Information"},{"key":"10.3233\/IDA-205306_ref11","doi-asserted-by":"crossref","unstructured":"M.W. Hao, H.L. Dai, K. Hao, C. Li, Y.J. Zhang and H.N. Song, Optimization of density-based K-means algorithm in trajectory data clustering, in: Proceedings of the International Wireless Internet Conference, 2017, pp. 440\u2013450.","DOI":"10.1007\/978-3-319-90802-1_39"},{"key":"10.3233\/IDA-205306_ref12","unstructured":"C.C. Aggarwal, On k-anonymity and the curse of dimensionality, in: Proceedings of 31\ud835\udc60\ud835\udc61 International Conference on Very Large Data Bases, 2005, pp. 901\u2013909."},{"key":"10.3233\/IDA-205306_ref13","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.ins.2011.07.035","article-title":"Privacy-preserving trajectory data publishing by local suppression","volume":"231","author":"Chen","year":"2013","journal-title":"Information Sciences"},{"issue":"2","key":"10.3233\/IDA-205306_ref14","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1109\/TSC.2013.55","article-title":"A novel time-obfuscated algorithm for trajectory privacy protection","volume":"7","author":"Hwang","year":"2013","journal-title":"IEEE Transactions on Services Computing"},{"issue":"1","key":"10.3233\/IDA-205306_ref15","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1145\/2031331.2031335","article-title":"Trajectory privacy in location-based services and data publication","volume":"13","author":"Chow","year":"2011","journal-title":"ACM Sigkdd Explorations Newsletter"},{"key":"10.3233\/IDA-205306_ref16","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.ins.2016.08.010","article-title":"Collaborative trajectory privacy preserving scheme in location-based services","volume":"387","author":"Peng","year":"2017","journal-title":"Information Sciences"},{"key":"10.3233\/IDA-205306_ref17","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.jnca.2015.01.004","article-title":"A fast privacy-preserving framework for continuous location-based queries in road networks","volume":"53","author":"Wang","year":"2015","journal-title":"Journal of Network and Computer Applications"},{"issue":"4","key":"10.3233\/IDA-205306_ref18","doi-asserted-by":"crossref","first-page":"121","DOI":"10.3390\/info10040121","article-title":"Location privacy protection systems in presence of service quality and energy constraints","volume":"10","author":"Tefera","year":"2019","journal-title":"Information"},{"key":"10.3233\/IDA-205306_ref19","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1016\/j.ins.2019.01.008","article-title":"Towards privacy preservation for \u201ccheck-in\u201d services in location-based social networks","volume":"481","author":"Sun","year":"2019","journal-title":"Information Sciences"},{"key":"10.3233\/IDA-205306_ref20","doi-asserted-by":"crossref","unstructured":"M. Terrovitis and N. Mamoulis, Privacy preservation in the publication of trajectories, in: Proceedings of the Ninth International Conference on Mobile Data Management, 2008, pp. 65\u201372.","DOI":"10.1109\/MDM.2008.29"},{"issue":"7","key":"10.3233\/IDA-205306_ref21","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1109\/TKDE.2017.2675420","article-title":"Local suppression and splitting techniques for privacy preserving publication of trajectories","volume":"29","author":"Terrovitis","year":"2017","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.3233\/IDA-205306_ref22","doi-asserted-by":"crossref","unstructured":"N. Mohammed, B. Fung, P.C.K. Hung and C.K. Lee, Anonymizing healthcare data: A case study on the blood transfusion service, in: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009, pp. 1285\u20131294.","DOI":"10.1145\/1557019.1557157"},{"issue":"4","key":"10.3233\/IDA-205306_ref23","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1007\/s11280-017-0489-2","article-title":"Personalized semantic trajectory privacy preservation through trajectory reconstruction","volume":"21","author":"Dai","year":"2018","journal-title":"World Wide Web"},{"key":"10.3233\/IDA-205306_ref24","doi-asserted-by":"crossref","unstructured":"N. Mohammed, R. Chen, B.C.M. Fung and P.S. Yu, Differentially private data release for data mining, in: Proceedings of the 17\ud835\udc61\u210e ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011, pp. 493\u2013501.","DOI":"10.1145\/2020408.2020487"},{"key":"10.3233\/IDA-205306_ref25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.swevo.2016.01.004","article-title":"Recent advances in differential evolution: an updated survey","volume":"27","author":"Das","year":"2016","journal-title":"Swarm and Evolutionary Computation"},{"key":"10.3233\/IDA-205306_ref26","doi-asserted-by":"crossref","unstructured":"X. Liu, L. Wang and Y. Zhu, SLAT: Sub-trajectory linkage attack tolerance framework for privacy-preserving trajectory publishing, in: Proceedings of the 2018 International Conference on Networking and Network Applications (NaNA), 2018, pp. 298\u2013303.","DOI":"10.1109\/NANA.2018.8648724"},{"issue":"4","key":"10.3233\/IDA-205306_ref27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1857947.1857950","article-title":"Centralized and distributed anonymization for high-dimensional healthcare data","volume":"4","author":"Mohammed","year":"2010","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"10.3233\/IDA-205306_ref28","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.ins.2012.04.015","article-title":"Microaggregation-and permutation-based anonymization of movement data","volume":"208","author":"Domingo-Ferrer","year":"2012","journal-title":"Information Sciences"},{"key":"10.3233\/IDA-205306_ref29","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.trc.2013.12.003","article-title":"Anonymizing trajectory data for passenger flow analysis","volume":"39","author":"Ghasemzadeh","year":"2014","journal-title":"Transportation Research Part C: Emerging Technologies"}],"container-title":["Intelligent Data Analysis"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDA-205306","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:19:10Z","timestamp":1777454350000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDA-205306"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,15]]},"references-count":29,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.3233\/ida-205306","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"value":"1088-467X","type":"print"},{"value":"1571-4128","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,15]]}}}