Trajectory privacy data publishing scheme based on local optimisation and R-tree

References

• Bai, Y., Li, X., Chen, C., Li, X., & Wang, Y. (2021). Research on optimal suppression differential privacy protection for trajectory data publishing. Journal of Chinese Computer Systems, 42(8), 6. https://doi.org/10.3969/j.issn.1000-1220.2021.08.034
   Google Scholar
• Bozkurt, Y., Braun, R., Rossmann, A., & Hertweck, D. (2020). The advent of smart cities: status quo and future research directions. https://dx.doi.org/10.33965/ict_csc_wbc_2020_202008l014
   Google Scholar
• Cheng, W., Wen, R., Huang, H., Miao, W., & Wang, C. (2022). OPTDP: Towards optimal personalized trajectory differential privacy for trajectory data publishing. Neurocomputing, 472, 201–211. https://doi.org/10.1016/j.neucom.2021.04.137
   Web of Science ®Google Scholar
• Cunha, M., Mendes, R., & Vilela, J. P. (2019, October). Clustering geo-indistinguishability for privacy of continuous location traces. In 2019 4th International Conference on Computing, Communications and Security (ICCCS) (pp. 1–8). IEEE. https://doi.org/10.1109/CCCS.2019.8888111
   Google Scholar
• Dwork, C. (2008, April). Differential privacy: A survey of results. In International Conference on Theory and Applications of Models of Computation (pp. 1–19). Springer.
   Google Scholar
• Ghosh, S., Ghosh, S. K., & Buyya, R. (2020). MARIO: A spatio-temporal data mining framework on google cloud to explore mobility dynamics from taxi trajectories. Journal of Network and Computer Applications, 164, Article 102692. https://doi.org/10.1016/j.jnca.2020.102692
   Web of Science ®Google Scholar
• Hu, S., Gao, S., Wu, L., Xu, Y., Zhang, Z., Cui, H., & Gong, X. (2021). Urban function classification at road segment level using taxi trajectory data: A graph convolutional neural network approach. Computers, Environment and Urban Systems, 87, Article 101619. https://doi.org/10.1016/j.compenvurbsys.2021.101619
   PubMed Web of Science ®Google Scholar
• Li, H., Xue, X., Li, Z., Li, L., & Xiong, J. (2021). Location privacy protection scheme for LBS in IoT. Wireless Communications and Mobile Computing. https://doi.org/10.1155/2021/9948543
   PubMed Web of Science ®Google Scholar
• Liu, M., Zhang, Z., Chai, W., Chai, W., & Wang, B. (2022). Privacy-preserving COVID-19 contact tracing solution based on blockchain. Computer Standards & Interfaces, 83, Article 103643. https://doi.org/10.1016/j.csi.2022.103643
   PubMed Web of Science ®Google Scholar
• Liu, X., Xie, L., Wang, Y., Zou, J., Xiong, J., Ying, Z., & Vasilakos, A. V. (2020a). Privacy and security issues in deep learning: A survey. IEEE Access, 9, 4566–4593. https://doi.org/10.1109/ACCESS.2020.3045078
   Web of Science ®Google Scholar
• Liu, Y., Feng, T., Peng, M., Jiang, Z., Xu, Z., Guan, J., & Yao, S. (2020b). COMP: Online control mechanism for profit maximization in privacy-preserving crowdsensing. IEEE Journal on Selected Areas in Communications, 38(7), 1614–1628. https://doi.org/10.1109/JSAC.2020.2999697
   Web of Science ®Google Scholar
• Mohammed, N., Fung, B., & Debbabi, M. (2010). Preserving privacy and utility in RFID data publishing.
   Google Scholar
• Niu, T., Qing, L., Han, L., Long, Y., Hou, J., Li, L., Tang, W., & Teng, Q. (2022). Small public space vitality analysis and evaluation based on human trajectory modeling using video data. Building and Environment, 225, Article 109563. https://doi.org/10.1016/j.buildenv.2022.109563
   Web of Science ®Google Scholar
• Qiu, S. Y., Pi, D., Wang, Y. X., & Liu, Y. F. (2023). Novel trajectory privacy protection method against prediction attacks. Expert Systems with Applications, 213, Article 118870. https://doi.org/10.1016/j.eswa.2022.118870
   Web of Science ®Google Scholar
• Tedjopurnomo, D. A., Li, X., Bao, Z., Cong, G., Choudhury, F., & Qin, A. K. (2021). Similar trajectory search with spatio-temporal deep representation learning. ACM Transactions on Intelligent Systems and Technology, 12(6), 1–26. https://doi.org/10.1145/3466687
   PubMed Web of Science ®Google Scholar
• Terrovitis, M., Poulis, G., Mamoulis, N., & Skiadopoulos, S. (2017). Local suppression and splitting techniques for privacy preserving publication of trajectories. IEEE Transactions on Knowledge and Data Engineering, 29(7), 1466–1479. https://doi.org/10.1109/TKDE.2017.2675420
   Web of Science ®Google Scholar
• Tu, W., Zhu, T., Xia, J., Zhou, Y., Lai, Y., Jiang, J., & Li, Q. (2020). Portraying the spatial dynamics of urban vibrancy using multisource urban big data. Computers, Environment and Urban Systems, 80, Article 101428. https://doi.org/10.1016/j.compenvurbsys.2019.101428
   Web of Science ®Google Scholar
• Wang, S., Bao, Z., Culpepper, J. S., & Cong, G. (2022). A survey on trajectory data management, analytics, and learning. ACM Computing Surveys, 54(2), 1–36. https://doi.org/10.1145/3440207
   Web of Science ®Google Scholar
• Wu, W., Niu, X., & Li, M. (2021). Influence of built environment on street vitality: A case study of west Nanjing road in Shanghai based on mobile location data. Sustainability, 13. https://doi.org/10.3390/su13041840
   Web of Science ®Google Scholar
• Yuan, S., Pi, D., Zhao, X., & Xu, M. (2021). Differential privacy trajectory data protection scheme based on R-tree. Expert Systems with Applications, 182, Article 115215. https://doi.org/10.1016/j.eswa.2021.115215
   Web of Science ®Google Scholar
• Zhang, S., Yao, T., Sandor, V. K. A., Weng, T. H., & Su, J. (2021). A novel blockchain-based privacy-preserving framework for online social networks. Connection Science, 33, 555–575. https://doi.org/10.1080/09540091.2020.1854181
   Web of Science ®Google Scholar
• Zhang, X., Chen, C., Xie, Y., Chen, X., Zhang, J., & Xiang, Y. (2022). A survey on privacy inference attacks and defenses in cloud-based deep neural network. Computer Standards & Interfaces, 83, Article 103672. https://doi.org/10.1016/j.csi.2022.103672
   PubMed Web of Science ®Google Scholar
• Zhao, X., Pi, D., & Chen, J. (2020). Novel trajectory privacy-preserving method based on prefix tree using differential privacy. Knowledge-Based Systems, 198, 105940. https://doi.org/10.1016/j.knosys.2020.105940
   Web of Science ®Google Scholar
• Zuo, K., Liu, R., Zhao, J., Shen, Z., & Chen, F., (2022). Method for the protection of spatiotemporal correlation location privacy with semantic information. Journal of Xidian University, 49(1), 67–77. https://doi.org/10.19665/j.issn1001-2400.2022.01.007
   Google Scholar