{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T05:37:28Z","timestamp":1741153048653,"version":"3.38.0"},"reference-count":29,"publisher":"EDP Sciences","license":[{"start":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T00:00:00Z","timestamp":1738195200000},"content-version":"vor","delay-in-days":29,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2022YFB4501500","2022YFB4501503"],"award-info":[{"award-number":["2022YFB4501500","2022YFB4501503"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Security and Safety"],"accepted":{"date-parts":[[2024,10,24]]},"published-print":{"date-parts":[[2025]]},"abstract":"<jats:p>With the explosive growth of Location-Based Services (LBS), a substantial amount of geolocation data, containing end-user private information, is amassed, posing severe privacy risks. Trajectory-User Linking (TUL) is a trajectory mining task aimed at linking trajectories to their generators. Recent research has introduced deep learning-based TUL models. However, these models face challenges related to limited data quality and inadequate extraction of bidirectional and multi-topic semantic information from trajectories. In this study, we propose Trajectory-User Linking via Supervised Encoding (TULSE), centered on supervised encoding of location points and trajectories to address the TUL task. Specifically, TULSE extracts spatial and temporal information from location points through a novel method named Supervised Spatiotemporal Encoding. Additionally, TULSE employs a BiLSTM with multi-head attention to capture bidirectional and multi-topic semantics from trajectories. Furthermore, recognizing the limitations of current evaluation metrics, we introduce a novel metric named Hierarchical Privacy Loss (HPL). HPL offers a more detailed assessment of TUL solutions by statistically analyzing the distribution of prediction accuracy among users. We conduct extensive experiments on two benchmark datasets, and empirical results show that TULSE outperforms existing TUL methods.<\/jats:p>","DOI":"10.1051\/sands\/2024018","type":"journal-article","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T18:47:12Z","timestamp":1729795632000},"page":"2024018","source":"Crossref","is-referenced-by-count":0,"title":["Trajectory-user linking via supervised encoding"],"prefix":"10.1051","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5693-2742","authenticated-orcid":false,"given":"Chengrui","family":"Hu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4466-7523","authenticated-orcid":false,"given":"Zheng","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0867-6308","authenticated-orcid":false,"given":"Siyuan","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Bowen","family":"Shu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4415-3676","authenticated-orcid":false,"given":"Min","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Li","sequence":"additional","affiliation":[]}],"member":"250","published-online":{"date-parts":[[2025,1,30]]},"reference":[{"key":"R1","first-page":"1689","volume":"17","author":"Gao","year":"2017","journal-title":"IJCAI"},{"key":"R2","doi-asserted-by":"crossref","unstructured":"Das G, Gunopulos D and Mannila H. Finding similar time series. In: European Symposium on Principles of Data Mining and Knowledge Discovery. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997, 88\u2013100.","DOI":"10.1007\/3-540-63223-9_109"},{"key":"R3","unstructured":"Yi BK, Jagadish HV and Faloutsos C. Efficient retrieval of similar time sequences under time warping. In: Proceedings 14th International Conference on Data Engineering. IEEE, 1998, 201\u2013208."},{"key":"R4","unstructured":"Mikolov T, Chen K and Corrado G et al. Efficient estimation of word representations in vector space. arXiv preprint https:\/\/arxiv.org\/abs\/1301.3781, 2013."},{"key":"R5","doi-asserted-by":"crossref","unstructured":"Sun T, Xu Y and Wang F et al. Trajectory-user link with attention recurrent networks. In: 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021, 4589\u20134596.","DOI":"10.1109\/ICPR48806.2021.9412453"},{"key":"R6","doi-asserted-by":"crossref","first-page":"112103","DOI":"10.1007\/s11432-021-3673-6","volume":"67","author":"Li","year":"2024","journal-title":"Sci China Inf Sci"},{"key":"R7","doi-asserted-by":"crossref","first-page":"72125","DOI":"10.1109\/ACCESS.2018.2881457","volume":"6","author":"Wang","year":"2018","journal-title":"IEEE Access"},{"key":"R8","doi-asserted-by":"crossref","unstructured":"Yu Y, Tang H and Wang F et al. Tulsn: siamese network for trajectory-user linking. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE 2020; 1\u20138.","DOI":"10.1109\/IJCNN48605.2020.9206609"},{"key":"R9","unstructured":"Miao C, Wang J and Yu H et al. Trajectory-user linking with attentive recurrent network. In: Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020, 878\u2013886."},{"key":"R10","doi-asserted-by":"crossref","unstructured":"Chen W, Li S and Huang C et al. Mutual distillation learning network for trajectory-user linking. arXiv preprint https:\/\/arxiv.org\/abs\/2205.03773, 2022.","DOI":"10.24963\/ijcai.2022\/274"},{"key":"R11","unstructured":"Freudiger J, Shokri R and Hubaux JP. Evaluating the privacy risk of location-based services. In: Financial Cryptography and Data Security: 15th International Conference, FC 2011, Gros Islet, St. Lucia, February 28-March 4, 2011, Revised Selected Papers 15. Springer Berlin Heidelberg, 2012, 31\u201346."},{"key":"R12","doi-asserted-by":"crossref","unstructured":"Zang H and Bolot J. Anonymization of location data does not work: A large-scale measurement study. In: Proceedings of the 17th Annual International Conference on Mobile Computing and Networking, 2011, 145\u2013156.","DOI":"10.1145\/2030613.2030630"},{"key":"R13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/srep01376","volume":"3","author":"De Montjoye","year":"2013","journal-title":"Sci Rep"},{"key":"R14","doi-asserted-by":"crossref","unstructured":"Chen L, \u00d6zsu MT and Oria V. Robust and fast similarity search for moving object trajectories. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, 2005, 491\u2013502.","DOI":"10.1145\/1066157.1066213"},{"key":"R15","doi-asserted-by":"crossref","unstructured":"Xiao X, Zheng Y and Luo Q et al. Finding similar users using category-based location history. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2010, 442\u2013445.","DOI":"10.1145\/1869790.1869857"},{"key":"R16","doi-asserted-by":"crossref","unstructured":"Ashbrook D and Starner T. Learning significant locations and predicting user movement with GPS. In: Proceedings. Sixth International Symposium on Wearable Computers. IEEE, 2002, 101\u2013108.","DOI":"10.1109\/ISWC.2002.1167224"},{"key":"R17","doi-asserted-by":"crossref","unstructured":"Chen Z, Fu Y and Zhang M et al. The de-anonymization method based on user spatio-temporal mobility trace. In: Information and Communications Security: 19th International Conference, ICICS 2017, Beijing, China, December 6-8, 2017, Proceedings 19. Springer International Publishing, 2018, 459\u2013471.","DOI":"10.1007\/978-3-319-89500-0_40"},{"key":"R18","doi-asserted-by":"crossref","unstructured":"Huo Z, Meng X and Zhang R. Feel free to check-in: Privacy alert against hidden location inference attacks in GeoSNs. In: Database Systems for Advanced Applications: 18th International Conference, DASFAA 2013, Wuhan, China, April 22-25, 2013. Proceedings, Part I 18. Springer Berlin Heidelberg, 2013, 377\u2013391.","DOI":"10.1007\/978-3-642-37487-6_29"},{"key":"R19","doi-asserted-by":"crossref","unstructured":"Sadilek A, Kautz H and Bigham JP. Finding your friends and following them to where you are. In: Proceedings of the fifth ACM International Conference on Web Search and Data Mining, 2012, 723\u2013732.","DOI":"10.1145\/2124295.2124380"},{"key":"R20","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TSMC.2014.2327053","volume":"45","author":"Yang","year":"2014","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"R21","doi-asserted-by":"crossref","unstructured":"Zhou F, Gao Q and Trajcevski G et al. Trajectory-User Linking via Variational AutoEncoder. IJCAI, 2018, 3212\u20133218.","DOI":"10.24963\/ijcai.2018\/446"},{"key":"R22","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans Inf Theory"},{"key":"R23","first-page":"1","volume":"70","author":"Hu","year":"2021","journal-title":"IEEE Trans Instrum Measur"},{"key":"R24","doi-asserted-by":"crossref","unstructured":"Cho E, Myers SA and Leskovec J. Friendship and mobility: User movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011, 1082\u20131090.","DOI":"10.1145\/2020408.2020579"},{"key":"R25","doi-asserted-by":"crossref","unstructured":"Zheng Y, Zhang L and Xie X et al. Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th International Conference on World Wide Web, 2009, 791\u2013800.","DOI":"10.1145\/1526709.1526816"},{"key":"R26","doi-asserted-by":"crossref","unstructured":"Zheng Y, Li Q and Chen Y et al. Understanding mobility based on GPS data. In: Proceedings of the 10th International Conference on Ubiquitous Computing, 2008, 312\u2013321.","DOI":"10.1145\/1409635.1409677"},{"key":"R27","first-page":"32","volume":"33","author":"Zheng","year":"2010","journal-title":"IEEE Data Eng Bull"},{"key":"R28","unstructured":"Kingma DP and Ba J. Adam: A method for stochastic optimization. arXiv preprint https:\/\/arxiv.org\/abs\/1412.6980, 2014."},{"key":"R29","first-page":"1929","volume":"15","author":"Srivastava","year":"2014","journal-title":"J Mach Learn Res"}],"container-title":["Security and Safety"],"original-title":[],"link":[{"URL":"https:\/\/sands.edpsciences.org\/10.1051\/sands\/2024018\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T12:46:12Z","timestamp":1741092372000},"score":1,"resource":{"primary":{"URL":"https:\/\/sands.edpsciences.org\/10.1051\/sands\/2024018"}},"subtitle":[],"editor":[{"given":"Dengguo","family":"Feng","sequence":"first","affiliation":[]},{"given":"Jian","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":29,"alternative-id":["sands20240018"],"URL":"https:\/\/doi.org\/10.1051\/sands\/2024018","relation":{},"ISSN":["2826-1275"],"issn-type":[{"type":"electronic","value":"2826-1275"}],"subject":[],"published":{"date-parts":[[2025]]}}}