{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T06:43:57Z","timestamp":1772693037706,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T00:00:00Z","timestamp":1772496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872190"],"award-info":[{"award-number":["61872190"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010242","name":"Jiangsu Planned Projects for Postdoctoral Research Funds","doi-asserted-by":"crossref","award":["2020Z058"],"award-info":[{"award-number":["2020Z058"]}],"id":[{"id":"10.13039\/501100010242","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Stable Supporting Fund of National Key Laboratory of Autonomous Marine Vehicle Technology","award":["2024-HYHXQ-WDZC06"],"award-info":[{"award-number":["2024-HYHXQ-WDZC06"]}]},{"name":"Jiangsu Province Postgraduate Research and Practice Innovation Program","award":["SJCX24_0320"],"award-info":[{"award-number":["SJCX24_0320"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The proliferation of Location-Based Services (LBSs) has generated vast trajectory datasets that offer immense analytical value but pose critical privacy risks. Achieving an optimal balance between data utility and privacy preservation remains a challenge, a difficulty compounded by the limitations of existing methods in modeling complex, long-term spatiotemporal dependencies. To address this, this paper proposes a trajectory data publishing scheme combining a Transformer decoder with differential privacy. Unlike traditional single-layer approaches, the proposed method establishes a systematic generation\u2013generalization framework. First, a Transformer decoder is integrated into a Generative Adversarial Network (GAN). This architecture mitigates the gradient vanishing issues common in RNN-based models, generating high-fidelity synthetic trajectories that capture long-range correlations while decoupling them from sensitive source data. Second, to provide rigorous privacy guarantees, a clustering-based generalization strategy is implemented, utilizing Exponential and Laplace mechanisms to ensure \u03f5-differential privacy. Experiments on the Geolife and Foursquare NYC datasets demonstrate that the scheme significantly outperforms leading baselines, achieving a superior trade-off between privacy protection and data utility.<\/jats:p>","DOI":"10.3390\/ijgi15030106","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T12:48:56Z","timestamp":1772542136000},"page":"106","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Trajectory Data Publishing Scheme Based on Transformer Decoder and Differential Privacy"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8543-8607","authenticated-orcid":false,"given":"Haiyong","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"},{"name":"Smart Campus Research Centre, Information Construction and Management Office, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"},{"name":"National Key Laboratory of Autonomous Marine Vehicle Technology Laboratory, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7418-6657","authenticated-orcid":false,"given":"Wei","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gramaglia, M., Fiore, M., Tarable, A., and Banchs, A. (2017, January 1\u20134). Preserving mobile subscriber privacy in open datasets of spatiotemporal trajectories. Proceedings of the IEEE INFOCOM 2017\u2014IEEE Conference on Computer Communications, Atlanta, GA, USA.","DOI":"10.1109\/INFOCOM.2017.8056979"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"399","DOI":"10.3182\/20060517-3-FR-2903.00211","article-title":"Mining public transport user behavior from smart card data","volume":"39","author":"Agard","year":"2006","journal-title":"IFAC Proc. Vol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2743025","article-title":"Trajectory Data Mining: An Overview","volume":"6","author":"Zheng","year":"2015","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103951","DOI":"10.1016\/j.jnca.2024.103951","article-title":"Privacy-preserving generation and publication of synthetic trajectory microdata: A comprehensive survey","volume":"230","author":"Kim","year":"2024","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1142\/S0218488502001648","article-title":"k-anonymity: A Model for protecting privacy","volume":"10","author":"Sweeney","year":"2002","journal-title":"Int. J. Uncertain Fuzziness Knowl. Based Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Dwork, C. (2006). Differential privacy. Proceedings of the 33rd International Colloquium on Automata, Languages and Programming, Venice, Italy, 10\u201314 July 2006, Springer.","DOI":"10.1007\/11787006_1"},{"key":"ref_7","first-page":"11","article-title":"Differentially Private Trajectory Data Publication","volume":"22","author":"Chen","year":"2011","journal-title":"Comput. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, R., Acs, G., and Castelluccia, C. (2012). Differentially private sequential data publication via variable-length n-grams. Proceedings of the 2012 ACM Conference on Computer and Communications Security, ACM.","DOI":"10.1145\/2382196.2382263"},{"key":"ref_9","unstructured":"Hua, J., Gao, Y., and Zhong, S. (May, January 26). Differentially private publication of general time serial trajectory data. Proceedings of the 24th IEEE Conference on Computer Communications, Hong Kong, China."},{"key":"ref_10","first-page":"1","article-title":"Achieving differential privacy of trajectory data publishing in participatory sensing","volume":"400\u2013401","author":"Li","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"105940","DOI":"10.1016\/j.knosys.2020.105940","article-title":"Novel trajectory privacy-preserving method based on prefix tree using differential privacy","volume":"198","author":"Zhao","year":"2020","journal-title":"Knowl. Based Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"115215","DOI":"10.1016\/j.eswa.2021.115215","article-title":"Differential privacy trajectory data protection scheme based on R-tree","volume":"182","author":"Yuan","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4006","DOI":"10.1109\/TIFS.2023.3290486","article-title":"TCPP: Achieving Privacy-Preserving Trajectory Correlation with Differential Privacy","volume":"18","author":"Wu","year":"2023","journal-title":"IEEE Trans. Inf. Forens. Secur."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8387","DOI":"10.1109\/JIOT.2020.3004826","article-title":"Edge\u2013Cloud-Aided Differentially Private Tucker Decomposition for Cyber\u2013Physical\u2013Social Systems","volume":"9","author":"Feng","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5451","DOI":"10.1109\/TII.2025.3555993","article-title":"Privacy-Preserving Recommendations With Mixture Model-Based Matrix Factorization Under Local Differential Privacy","volume":"21","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.14778\/2809974.2809978","article-title":"DP T: Differentially private trajectory synthesis using hierarchical reference systems","volume":"8","author":"He","year":"2015","journal-title":"Proc. VLDB Endow."},{"key":"ref_17","unstructured":"Wang, H., Zhang, Z., Wang, T., He, S., Backes, M., Chen, J., and Zhang, Y. (2023). PrivTrace: Differentially Private Trajectory Synthesis by Adaptive Markov Models. Proceedings of the 32nd USENIX Security Symposium (USENIX Security 23), USENIX Association."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102736","DOI":"10.1016\/j.jnca.2020.102736","article-title":"RNN-DP: A new differential privacy scheme base on Recurrent Neural Network for Dynamic trajectory privacy protection","volume":"168","author":"Chen","year":"2020","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_19","first-page":"1","article-title":"LSTM-TrajGAN: A deep learning approach to trajectory privacy protection","volume":"Volume 12","author":"Rao","year":"2021","journal-title":"Proceedings of the 11th International Conference on Geographic Information Science"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.future.2022.12.027","article-title":"DP-TrajGAN: A privacy-aware trajectory generation model with differential privacy","volume":"142","author":"Zhang","year":"2023","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_21","first-page":"1","article-title":"Location Privacy-preserving Mechanisms in Location based Services: A Comprehensive Survey","volume":"54","author":"Jiang","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"124264","DOI":"10.1016\/j.eswa.2024.124264","article-title":"BiGRU-DP: Improved differential privacy protection method for trajectory data publishing","volume":"252","author":"Shen","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.neucom.2021.04.137","article-title":"OPTDP: Towards optimal personalized trajectory differential privacy for trajectory data publishing","volume":"472","author":"Cheng","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_24","first-page":"54","article-title":"Trajectory privacy protection scheme based on differential privacy","volume":"42","author":"Chen","year":"2021","journal-title":"J. Commun."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Zhang, L., Xie, X., and Ma, W.Y. (2009). Mining Interesting Locations and Travel Sequences from GPS Trajectories. Proceedings of the 18th International Conference on World Wide Web, WWW \u201909, Madrid, Spain, 20\u201324 April 2009, Association for Computing Machinery.","DOI":"10.1145\/1526709.1526816"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.future.2023.01.008","article-title":"Hasse sensitivity level: A sensitivity-aware trajectory privacy-enhanced framework with Reinforcement Learning","volume":"142","author":"Zhang","year":"2023","journal-title":"Future Gener. Comput. Syst."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/15\/3\/106\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T05:30:12Z","timestamp":1772688612000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/15\/3\/106"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,3]]},"references-count":26,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["ijgi15030106"],"URL":"https:\/\/doi.org\/10.3390\/ijgi15030106","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,3]]}}}