Abstract
Fog computing has been introduced for extending cloud computing to the edge of the network, which brings features and services closer to end-users. Despite all the benefits provided, fog computing still suffers from security and privacy issues. Location privacy has a critical issue since fog nodes collect sensitive data. The users continuously send queries to the Location-based service (LBS) server, which may cause vulnerabilities where an attacker may track users. Subsequently, location privacy is not adequate to preserve privacy and attackers can still deduce the movement pattern of the user. Therefore, trajectory privacy has been used to provide better protection for the whole trajectory of the user. Meanwhile, most of the existing researches did not protect the future location of the user, while attackers may predict or estimate the users’ next position if the geographical environment constraints are not considered and the historical data of the user not protected. To resolve the addressed issues and provide better privacy, we developed an approach for preserving the user’s future trajectory privacy by predicting the future location of the user using Extended Mobility Markov Chain (n-MMC) and then generating dummy trajectories by Dummy Rotation Algorithm. The results show that the system can achieve privacy preservation of the future trajectory of the user with an average accuracy of 60%.
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References
Hong, H.J.: From cloud computing to fog computing: unleash the power of edge and end devices. In: Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom, vol. 2017, pp. 331–334 (2017)
Stojmenovic, I., Wen, S., Huang, X., Luan, H.: An overview of Fog computing and its security issues. Concurr. Comput. Pract. Exp. 28(10), 2991–3005 (2016)
Mukherjee, M., et al.: Security and privacy in fog computing: challenges. IEEE Access 5, 19293–19304 (2017)
Wernke, M., Skvortsov, P., Dürr, F., Rothermel, K.: A classification of location privacy attacks and approaches. Pers. Ubiquitous Comput. 18(1), 163–175 (2012). https://doi.org/10.1007/s00779-012-0633-z
Chow, C.Y., Mokbel, M.F.: Trajectory privacy in location-based services and data publication. ACM SIGKDD Explor. Newsl. 13(1), 19 (2011)
Wu, R., Luo, G., Shao, J., Tian, L., Peng, C.: Location prediction on trajectory data: a review. Big Data Min. Anal. 1(2), 108–127 (2018)
Petzold, J., Bagci, F., Trumler, W., Ungerer, T.: Comparison of different methods for next location prediction. In: Nagel, W.E., Walter, W.V., Lehner, W. (eds.) Euro-Par 2006. LNCS, vol. 4128, pp. 909–918. Springer, Heidelberg (2006). https://doi.org/10.1007/11823285_96
Qiao, S., Shen, D., Wang, X., Han, N., Zhu, W.: A self-adaptive parameter selection trajectory prediction approach via hidden Markov models. IEEE Trans. Intell. Transp. Syst. 16(1), 284–296 (2015)
Qiao, Y., Si, Z., Zhang, Y., Ben Abdesslem, F., Zhang, X., Yang, J.: A hybrid Markov-based model for human mobility prediction. Neurocomputing 278, 99–109 (2018)
Du, Y., Wang, C., Qiao, Y., Zhao, D., Guo, W.: A geographical location prediction method based on continuous time series Markov model. PLoS One 13(11), e0207063 (2018)
Chen, M., Yu, X., Liu, Y.: Mining moving patterns for predicting next location. Inf. Syst. 54, 156–168 (2015)
Gambs, S., Killijian, M.O., Del Prado Cortez, M.N.: Next place prediction using mobility Markov chains. In: Proceedings of the First Workshop on Measurement, Privacy, and Mobility - MPM 2012, pp. 1–6 (2012)
Hwang, R.H., Hsueh, Y.L., Chung, H.W.: A novel time-obfuscated algorithm for trajectory privacy protection. IEEE Trans. Serv. Comput. 7(2), 126–139 (2014)
Memon, I., Chen, L., Arain, Q.A., Memon, H., Chen, G.: Pseudonym changing strategy with multiple mix zones for trajectory privacy protection in road networks. Int. J. Commun Syst 31(1), e3437 (2018)
Liao, D., Li, H., Sun, G., Anand, V.: Protecting user trajectory in location-based services. In: 2015 IEEE Global Communications Conference (GLOBECOM), pp. 1–6 (2015)
Gao, S., Ma, J., Sun, C., Li, X.: Balancing trajectory privacy and data utility using a personalized anonymization model. J. Netw. Comput. Appl. 38, 125–134 (2014)
Peng, T., Liu, Q., Meng, D., Wang, G.: Collaborative trajectory privacy preserving scheme in location-based services. Inf. Sci. (Ny) 387, 165–179 (2017)
Niu, B., Gao, S., Li, F., Li, H., Lu, Z.: Protection of location privacy in continuous LBSs against adversaries with background information. In: 2016 International Conference on Computing, Networking and Communications (ICNC), pp. 1–6 (2016)
Wang, T., et al.: Trajectory privacy preservation based on a fog structure for cloud location services. IEEE Access 5, 7692–7701 (2017)
Hayashida, S., Amagata, D., Hara, T., Xie, X.: Dummy generation based on user-movement estimation for location privacy protection. IEEE Access 6, 22958–22969 (2018)
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AlHamed, S., AlRodhaan, M., Tian, Y. (2020). Privacy Preservation of Future Trajectory Using Dummy Rotation Algorithm in Fog Computing. In: Tian, Y., Ma, T., Khan, M. (eds) Big Data and Security. ICBDS 2019. Communications in Computer and Information Science, vol 1210. Springer, Singapore. https://doi.org/10.1007/978-981-15-7530-3_38
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DOI: https://doi.org/10.1007/978-981-15-7530-3_38
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