Abstract
In general, generalization is a common strategy used for preserving the user’s privacy in location-based service (LBS). In this strategy, at least k−1 anonymous users are selected to generalize the real location. However, in some special areas, there will be too many anonymous users to be selected in a limited space, and the real location can be correlated with the specified area and violates the privacy. Therefore, in this paper, in order to cope with problem mentioned above, a random sensitive area based privacy preservation algorithm is proposed. In this algorithm, before selecting anonymous users, several random sensitive areas are selected. Then based on the selected areas, anonymous users are selected to generalize the real location. With these operations, the real location is not only generalized by anonymous users but also generalized by sensitive areas with different types, so the adversary will be even more difficult to identify the real location. At last, security analysis as well as simulation experiments are given to further demonstrate the superiority of algorithm proposed in the level of privacy preservation and the capability of execution efficiency.
Similar content being viewed by others
References
Zhang, L., Li, J., Yang, S., & Wang, B. (2017). Privacy preserving in cloud environment for obstructed shortest path query. Wireless Personal Communications, 96(2), 2305–2322.
Furini, M., Mirri, S., Montangero, M., & Prandi, C. (2020). Privacy perception when using smartphone applications. Mobile Networks & Applications, 25(3), 1055–1061.
Sun, Y., Zhang, L., Li, J., & Zhang, Z. (2020). A new grid partitioning technology for location privacy protection. Turkish Journal of Electrical Engineering and Computer Sciences, 28(6), 3438–3455.
Zhang, L., Yang, S., Li, J., & Yu, L. (2018). A particle swarm optimization clustering-based attribute generalization privacy protection scheme. Journal of Circuits, Systems and Computers, 27(11), 641–654.
Zhang, S., Li, X., Tan, Z., Peng, T., & Wang, G. (2019). A caching and spatial K-anonymity driven privacy enhancement scheme in continuous location-based services. Future Generation Computer Systems, 94, 40–50.
Gruteser, M., & Grunwald, D. (2003). Anonymous usage of location-based services through spatial and temporal cloaking. pp. 31–42.
Zou, S. H., Xi, J. W., Wang, H. G., & Xu, G. A. (2020). CrowdBLPS: A blockchain-based location-privacy-preserving mobile crowdsensing system. IEEE Transactions on Industrial Informatics, 16(6), 4206–4218.
Yang, M., Zhu, T., Liang, K., & Zhou, W. (2019). A blockchain-based location privacy-preserving crowdsensing system. Future Generation Computer Systems-The International Journal of Escience, 94, 408–418.
Zhang, S. B., Mao, X. J., Choo, K. K. R., Peng, T., & Wang, G. J. (2020). A trajectory privacy-preserving scheme based on a dual-K mechanism for continuous location-based services. Information Sciences, 527, 406–419.
Peng, T., Liu, Q., Meng, D. C., & Wang, G. J. (2017). Collaborative trajectory privacy preserving scheme in location-based services. Information Sciences, 387(2017), 165–179.
Kang, J., Steiert, D., Lin, D., & Fu, Y. J. (2020). MoveWithMe: Location privacy preservation for smartphone users. IEEE Transactions on Information Forensics and Security, 15, 711–724.
Shen, H., Zhang, M. W., Wang, H., Guo, F. C., & Susilo, W. (2020). A lightweight privacy-preserving fair meeting location determination scheme. IEEE Internet of Things Journal, 7(4), 3083–3093.
Lei, Z., Lili, H., Desheng, L., Jing, L., Qingfeng, J., & Qi, Y. (2019). An attribute generalization mix-zone without privacy leakage. IEEE Access, 7(1), 57088–57099.
Abdelharneed, S. A., Moussa, S. M., & Khalifa, M. E. (2019). Restricted sensitive attributes-based sequential anonymization (RSA-SA) approach for privacy-preserving data stream publishing. Knowledge-Based Systems, 164, 1–20.
Wang, J. B., Cai, Z. P., & Yu, J. G. (2020). Achieving personalized k-anonymity-based content privacy for autonomous vehicles in CPS. IEEE Transactions on Industrial Informatics, 16(6), 4242–4251.
Zhang, L., Ma, C., Yang, S., & Zheng, X. (2017). Probability indistinguishable: A query and location correlation attack resistance scheme. Wireless Personal Communications, 97(4), 6167–6187.
Bouchelagherm, S., & Omar, M. (2020). Secure and efficient pseudonymization for privacy-preserving vehicular communications in smart cities. Computers and Electrical Engineering, 82, 106557.
Lai, J., Mu, Y., Guo, F., Jiang, P., & Susilo, W. (2018). Privacy-enhanced attribute-based private information retrieval. Information Sciences, 454–455(2018), 275–291.
Xu, C., Xie, X., Zhu, L. H., Sharif, K., Zhang, C., Du, X. J., & Guizani, M. (2020). PPLS: A privacy-preserving location-sharing scheme in mobile online social networks. Science China-Information Sciences. https://doi.org/10.1007/s11432-019-1508-6.
Zhang, L., Chen, M., Liu, D., He, L., Wang, C., Sun, Y., & Wang, B. (2020). A ε-sensitive indistinguishable scheme for privacy preserving. Wireless Networks, 26(07), 5013–5033.
Zhang, Y. H., Li, M., Yang, D. J., Tang, J., Xue, G. L., & Xu, J. (2020). Tradeoff between location quality and privacy in crowdsensing: An optimization perspective. IEEE Internet of Things Journal, 7(4), 3535–3544.
Liu, Z. S., Zhang, L., Ni, W., & Collings, I. B. (2020). Uncoordinated pseudonym changes for privacy preserving in distributed networks. IEEE Transactions on Mobile Computing, 19(6), 1465–1477.
Li, W. H., Li, C., & Geng, Y. L. (2020). APS: Attribute-aware privacy-preserving scheme in location-based services. Information Sciences, 527, 460–476.
Li, W., Niu, B., Cao, J., Luo, Y., & Li, H. (2020). A personalized range-sensitive privacy-preserving scheme in LBSs. Concurrency and Computation: Practice and Experience, 32(5), e5462.
Talat, R., Obaidat, M. S., Muzammal, M., Sodhro, A. H., Luo, Z., & Pirbhulal, S. (2020). A decentralised approach to privacy preserving trajectory mining. Future Generation Computer Systems, 102, 382–392.
Luo, B., Li, X. H., Weng, J., Guo, J. J., & Ma, J. F. (2020). Blockchain enabled trust-based location privacy protection scheme in VANET. IEEE Transactions on Vehicular Technology, 69(2), 2034–2048.
Galyaev, A. A., Lysenko, P. V., & Yakhno, V. P. (2018). Optimal path planning for an object in a random search region. Automation and Remote Control, 79(11), 2080–2089.
Lei, Z., Chunguang, M., Songtao, Y., et al. (2017). CP-ABE based users collaborative privacy protection scheme for continuous query. Journal on Communications, 38(09), 76–85.
Niu, B., Zhu, X., Li, Q., & Chen, J. (2015). A novel attack to spatial cloaking schemes in location-based services. Future Generation Computer Systems, 2015(49), 125–132.
Mingyan, X., Hua, Z., Xinsheng, J., et al. (2018). Distribution-perceptive-based spatial cloaking algorithm for location privacy in mobile peer-to-peer enviroments. Journal of software, 29(07), 1852–1862.
Acknowledgments
This work was supported by the Basic Scientific Research Operating Expenses of Heilongjiang Provincial University and Colleges under Grant 2020-KYYWF-0227.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Liu, Y., Tian, J., Du, Y. et al. A Random Sensitive Area Based Privacy Preservation Algorithm for Location-Based Service. Wireless Pers Commun 119, 1179–1192 (2021). https://doi.org/10.1007/s11277-021-08256-y
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-021-08256-y