Abstract
In general, continuous queries in location-based service usually point to a predetermined destination, as the destination usually means the most sensitive point for a user, the process of moving will show a trend of sensitivity increasing. As a result, this trend can be used to infer the destination as well as some other personal privacy such as workspace and home addresses. In order to cope with this privacy issue, in this paper we first formalize the pattern of sensitivity increasing and study this pattern to discuss the effectiveness on inferring the security that disposed by several currently used privacy preservation schemes. Then, we propose a ε-sensitive indistinguishable scheme to address the attack of sensitivity inference. Specifically, the proposed scheme utilizes a grid of Voronoi diagram to depict the sensitive contours and adds dummies according to the conception of differential privacy to cloak the trend of sensitivity increasing. At last, we illustrate the security analysis to verify the capacity of privacy preservation and we also verify the performance of this scheme with experimental verification in both Euclidean space and road networks and compare it with other schemes.
Similar content being viewed by others
References
Zhao, P., Zhang, G., Wan, S., et al. (2019). A survey of local differential privacy for securing internet of vehicles. The Journal of Supercomputing. https://doi.org/10.1007/s11227-019-03104-0
Yin, C., et al. (2018). Location privacy protection based on differential privacy strategy for big data in industrial internet of things. IEEE Transactions on Industrial Informatics, 14(8), 3628–3636.
Zhao, P., Huang, H., Zhao, X., & Huang, D. (2020). P3: Privacy-preserving scheme against poisoning attacks in mobile-edge computing. IEEE Transactions on Computational Social Systems. https://doi.org/10.1109/TCSS.2019.2960824.
Lei, Z., et al. (2017). A real-time similar trajectories generation algorithm for trajectories differences identification resistance. Journal of Harbin Engineering University, 07, 1173–1178.
Ye, A. Y., Li, Y., & Xu, L. (2017). A novel location privacy-preserving scheme based on l-queries for continuous LBS. Computer Communications, 98, 1–10.
Zhao, P., et al. (2019). Synthesizing privacy preserving traces: Enhancing plausibility with social networks. Ieee-Acm Transactions on Networking, 27(6), 2391–2404.
Wang, S., et al. (2018). A trigger-based pseudonym exchange scheme for location privacy preserving in VANETs. Peer-to-Peer Networking and Applications, 11(3), 548–560.
Zhang, L., et al. (2018). A particle swarm optimization clustering-based attribute generalization privacy protection scheme. Journal of Circuits, Systems and Computers, 27(11), 641–654.
Lei, Z., et al. (2017). CP-ABE based users collaborative privacy protection scheme for continuous query. Journal on Communications, 38(09), 76–85.
Zhang, L. (2017). OTIBAAGKA: A new security tool for cryptographic mix-zone establishment in vehicular ad hoc networks. Ieee Transactions on Information Forensics and Security, 12(12), 2998–3010.
Chunguang, M., et al. (2017). Hiding yourself behind collaborative users when using continuous location-based services. Journal of Circuits, Systems and Computers, 26(07), 1750119:1–1750119:25.
Peng, T., et al. (2017). Collaborative trajectory privacy preserving scheme in location-based services. Information Sciences, 387, 165–179.
Fei, F., et al. (2017). A K-anonymity based schema for location privacy preservation. IEEE Transactions on Sustainable Computing, 4(2), 156–167.
Ghaffari, M., et al. (2017). P(4)QS: A peer-to-peer privacy preserving query service for location-based mobile applications. Ieee Transactions on Vehicular Technology, 66(10), 9458–9469.
Li, Z., Wang, J., & Zhang, W. (2019). Revisiting post-quantum hash proof systems over lattices for Internet of Thing authentications. Journal of Ambient Intelligence and Humanized Computing, 2019, 1–11.
Li, Z., & Wang, D. (2019). Achieving one-round password-based authenticated key exchange over lattices. IEEE Transactions on Services Computing. https://doi.org/10.1109/TSC.2019.2939836.
Palanisamy, B., & Liu, L. (2015). Attack-resilient mix-zones over road networks: Architecture and algorithms. IEEE Transactions on Mobile Computing, 14(3), 495–508.
Schlegel, R., et al. (2015). User-defined privacy grid system for continuous location-based services. IEEE Transactions on Mobile Computing, 14(10), 2158–2172.
Ni, W., Gu, M., & Chen, X. (2016). Location privacy-preserving k nearest neighbor query under user's preference. Knowledge-Based Systems, 103, 19–27.
Zhang, L. C., Cai, Z. P., & Wang, X. M. (2016). FakeMask: A novel privacy preserving approach for smartphones. Ieee Transactions on Network and Service Management, 13(2), 335–348.
Dewri, R., & Thurimella, R. (2014). Exploiting service similarity for privacy in location-based search queries. IEEE Transactions on Parallel and Distributed Systems, 25(2), 374–383.
Sun, G., et al. (2017). L2P2: A location-label based approach for privacy preserving in LBS. Future Generation Computer Systems, 74, 375–384.
Shokri, R., Theodorakopoulos, G., & Troncoso, C. (2017). Privacy games along location traces: A game-theoretic framework for optimizing location privacy. ACM Transactions on Privacy and Security, 19(4), 1–31.
Shen, H., et al. (2017). Protecting trajectory privacy: A user-centric analysis. Journal of Network and Computer Applications, 82, 128–139.
Montazeri, Z., Houmansadr, A., & Pishro-Nik, H. (2017). Achieving perfect location privacy in wireless devices using anonymization. Ieee Transactions on Information Forensics and Security, 12(11), 2683–2698.
Wang, X. F., Mu, Y., & Chen, R. M. (2016). One-round privacy-preserving meeting location determination for smartphone applications. Ieee Transactions on Information Forensics and Security, 11(8), 1723–1732.
Aivodji, U. M., et al. (2016). Meeting points in ridesharing: A privacy-preserving approach. Transportation Research Part C-Emerging Technologies, 72, 239–253.
Rabieh, K., Mahmoud, M., & Younis, M. (2017). Privacy-preserving route reporting schemes for traffic management systems. Ieee Transactions on Vehicular Technology, 66(3), 2703–2713.
Zhang, L., et al. (2017). Privacy preserving in cloud environment for obstructed shortest path query. Wireless Personal Communications, 96(2), 2305–2322.
Zhao, P., et al. (2018). ILLIA: Enabling k-anonymity-based privacy preserving against location injection attacks in continuous LBS queries. Ieee Internet of Things Journal, 5(2), 1033–1042.
Peng, Z., et al. (2019). Location correlated differential privacy protection based on mobile feature analysis. Ieee Access, 7, 54483–54496.
Zhang, L., et al. (2017). Probability indistinguishable: A query and location correlation attack resistance scheme. Wireless Personal Communications, 97(4), 6167–6187.
Wei, J., Lin, Y., Yao, X., & Zhang, J. (2019). Differential privacy-based location protection in spatial crowdsourcing. IEEE Transactions on Services Computing. https://doi.org/10.1109/TSC.2019.2920643
Wu, Y. C., et al. (2018). Differentially private trajectory protection based on spatial and temporal correlation. Chinese Journal of Computers, 41(02), 309–322.
Hua, J., et al. (2018). A geo-indistinguishable location perturbation mechanism for location-based services supporting frequent queries. Ieee Transactions on Information Forensics and Security, 13(5), 1155–1168.
Elsalamouny, E., & Gambs, S. (2018). Optimal noise functions for location privacy on continuous regions. International Journal of Information Security, 17(1), 1–18.
Al-Dhubhani, R., & Cazalas, J. M. (2017). An adaptive geo-indistinguishability mechanism for continuous LBS queries. Wireless Networks, 24, 1–19.
Gruteser, M., & Grunwald, D. (2003). Anonymous usage of location-based services through spatial and temporal cloaking. In Proceedings of the 1st international conference on Mobile systems, applications and services (pp. 31–42).
Fuyu, L., Hua, K.A., & Ying, C. (2009). Query l-diversity in location-based services. In Proceedings of mobile data management: systems, services and middleware (pp. 436–442).
Rebollo-Monedero, D., et al. (2010). Private location-based information retrieval through user collaboration. Computer Communications, 33(6), 762–774.
Khoshgozaran, A., Shahabi, C., & Shirani-Mehr, H. (2011). Location privacy: Going beyond K-anonymity, cloaking and anonymizers. Knowledge and Information Systems, 26(3), 435–465.
Grissa, M., Yavuz, A. A., & Hamdaoui, B. (2017). Preserving the location privacy of secondary users in cooperative spectrum sensing. Ieee Transactions on Information Forensics and Security, 12(2), 418–431.
Hashem, T., Kulik, L., & Zhang, R. (2013). Countering overlapping rectangle privacy attack for moving kNN queries. Information Systems, 38(3), 430–453.
Hwang, R.-H., Hsueh, Y.-L., & Chung, H.-W. (2014). A novel time-obfuscated algorithm for trajectory privacy protection. IEEE Transactions on Services Computing, 7(2), 126–139.
Lei, Z., et al. (2017). Correlation probability indistinguishable location privacy protection algorithm. Journal on Communications, 38(08), 37–49.
Niu, B., et al. (2014). Achieving k-anonymity in privacy-aware location-based services. In International conference on computer communications (pp. 754–762).
Acknowledgements
This work was supported by the Natural Science Fund of Heilongjiang Province for Outstanding Youth (YQ2019F018). Post Doctoral Fund Project in China (2019M661260). the Basic Scientific Research Operating Expenses of Heilongjiang Provincial Universities and Colleges(2018-KYYWF-0941). Excellent Discipline Team Project of Jiamusi University (JDXKTD-2019008). Special Doctor Scientific Research Fund Launch Project of Jiamusi University (JMSUZB2018-01).
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
Zhang, L., Chen, M., Liu, D. et al. A ε-sensitive indistinguishable scheme for privacy preserving. Wireless Netw 26, 5013–5033 (2020). https://doi.org/10.1007/s11276-020-02378-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11276-020-02378-0