Abstract
At present, the protection of user’s location privacy (especially mobile user’s location privacy) is widely concerned by academia and industry. Many experts and scholars have proposed several secure and efficient solutions to this problem. However, in this context, if a mobile user wants to obtain the services she/he wants, she/he needs to share her/his location information continuously with an untrusted third-party in user’s locations. This will cause privacy and security issue. To tackle this problem, in this paper, we apply local differential privacy (LDP) in supporting continuous location sharing among mobile users. Firstly, we put forward a new idea of using conditional random field (CRF) in model user’s mobility. Then, we combine \(\delta \)-location set and \(\varepsilon \)-LDP and advance a mechanism to support continuous location sharing. Finally, we conduct experiments on real data set to evaluate our proposed mechanism. The results show that our mechanism is more effective than planar isotropic mechanism (PIM).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Krumm, J.: A markov model for driver turn prediction. In: Society of Automotive Engineers (SAE) 2008 World Congress, April 2008. SAE 2008 World Congress, April 2008. https://www.microsoft.com/en-us/research/publication/markov-model-driver-turn-prediction/, lloyd L. Withrow Distinguished Speaker Award
Götz, M., Nath, S., Gehrke, J.: Maskit: privately releasing user context streams for personalized mobile applications. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, pp. 289–300 (2012)
Alvim, M.S., Chatzikokolakis, K., McIver, A., Morgan, C., Palamidessi, C., Smith, G.: Differential privacy. Presented at the (2020). https://doi.org/10.1007/978-3-319-96131-6_23
Sun, G., Song, L., Liao, D., Yu, H., Chang, V.: Towards privacy preservation for “check-in" services in location-based social networks. Inf. Sci. 481, 616–634 (2019)
Peng, T., Liu, Q., Meng, D., Wang, G.: Collaborative trajectory privacy preserving scheme in location-based services. Inf. Sci. 387, 165–179 (2017)
Location Privacy in Mobile Applications. SCSSN. Springer, Singapore (2018). https://doi.org/10.1007/978-981-13-1705-7_6
Freudiger, J., Shokri, R., Hubaux, J.-P.: On the optimal placement of mix zones. In: Goldberg, I., Atallah, M.J. (eds.) PETS 2009. LNCS, vol. 5672, pp. 216–234. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03168-7_13
Gedik, B., Liu, L.: Protecting location privacy with personalized k-anonymity: architecture and algorithms. IEEE Trans. Mob. Comput. 7(1), 1–18 (2007)
Wu, X., Li, S., Yang, J., Dou, W.: A cost sharing mechanism for location privacy preservation in big trajectory data. In: 2017 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2017)
Andrés, M.E., Bordenabe, N.E., Chatzikokolakis, K., Palamidessi, C.: Geo-indistinguishability: differential privacy for location-based systems. In: Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications security, pp. 901–914 (2013)
Wang, T., Cao, Z., Wang, S., Wang, J., Qi, L., Liu, A., Xie, M., Li, X.: Privacy-enhanced data collection based on deep learning for internet of vehicles. IEEE Trans. Industr. Inf. 16(10), 6663–6672 (2019)
Wang, T., Jia, W., Xing, G., Li, M.: Exploiting statistical mobility models for efficient wi-fi deployment. IEEE Trans. Veh. Technol. 62(1), 360–373 (2012)
Chen, M., Wang, T., Ota, K., Dong, M., Zhao, M., Liu, A.: Intelligent resource allocation management for vehicles network: an a3c learning approach. Comput. Commun. 151, 485–494 (2020)
Xie, M., Ruan, Y., Hong, H., Shao, J.: A cp-abe scheme based on multi-authority in hybrid clouds for mobile devices. Futur. Gener. Comput. Syst. 121, 114–122 (2021)
Ardagna, C.A., Livraga, G., Samarati, P.: Protecting privacy of user information in continuous location-based services. In: 2012 IEEE 15th International Conference on Computational Science and Engineering, pp. 162–169. IEEE (2012)
Xiao, Y., Xiong, L.: Protecting locations with differential privacy under temporal correlations. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1298–1309 (2015)
Duchi, J.C., Jordan, M.I., Wainwright, M.J.: Local privacy and statistical minimax rates. In: 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, pp. 429–438. IEEE (2013)
Warner, S.L.: Randomized response: a survey technique for eliminating evasive answer bias. J. Am. Stat. Assoc. 60(309), 63–69 (1965)
Kairouz, P., Oh, S., Viswanath, P.: Extremal mechanisms for local differential privacy. Adv. Neural. Inf. Process. Syst. 27, 2879–2887 (2014)
Wang, T., Blocki, J., Li, N., Jha, S.: Locally differentially private protocols for frequency estimation. In: 26th \(\{\)USENIX\(\}\) Security Symposium (\(\{\)USENIX\(\}\) Security 17), pp. 729–745 (2017)
Wang, T., Li, N., Jha, S.: Locally differentially private frequent itemset mining. In: 2018 IEEE Symposium on Security and Privacy (SP), pp. 127–143. IEEE (2018)
Chen, R., Li, H., Qin, A.K., Kasiviswanathan, S.P., Jin, H.: Private spatial data aggregation in the local setting. In: 2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp. 289–300. IEEE (2016)
Sei, Y., Ohsuga, A.: Differential private data collection and analysis based on randomized multiple dummies for untrusted mobile crowdsensing. IEEE Trans. Inf. Forensics Secur. 12(4), 926–939 (2016)
McCallum, A.: Efficiently inducing features of conditional random fields. arXiv preprint arXiv:1212.2504 (2012)
Song, C., Qu, Z., Blumm, N., Barabási, A.L.: Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010)
Assam, R., Seidl, T.: Context-based location clustering and prediction using conditional random fields. In: Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia, pp. 1–10 (2014)
Kairouz, P., Bonawitz, K., Ramage, D.: Discrete distribution estimation under local privacy. In: International Conference on Machine Learning, pp. 2436–2444. PMLR (2016)
Erlingsson, Ú., Pihur, V., Korolova, A.: Rappor: randomized aggregatable privacy-preserving ordinal response. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pp. 1054–1067 (2014)
Zheng, Y., Xie, X., Ma, W.Y., et al.: Geolife: A collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 33(2), 32–39 (2010)
Acknowledgement
This work is supported by the National Natural Science Foundation of China (Grant Nos. 61602408, 61972352, 61572435), the Key Research and Development Program of Zhejiang Province (Grant No. 2021C03150) and Zhejiang Provincial Natural Science Foundation of China under Grant (Nos. LY19F020005, LY18F020009).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhu, L., Hong, H., Xie, M. (2022). A Novel Protection Method of Continuous Location Sharing Based on Local Differential Privacy and Conditional Random Field. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13155. Springer, Cham. https://doi.org/10.1007/978-3-030-95384-3_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-95384-3_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-95383-6
Online ISBN: 978-3-030-95384-3
eBook Packages: Computer ScienceComputer Science (R0)