Abstract
Various mobile devices facilitate the users’ life, but the issues are brought into privacy focus by the individuals. This paper aims at the protection of intimate relationships among users. We consider the intimacy of user relationships based on similar sub-trajectories between the users. Then, we propose a \(k_{mn}\)-anonymity protection model. We generalize from two aspects: location and time. The first is location generalization. The range that user pass within the time that the location point stays is the generalization region, and the corresponding location point in the region’s trajectory is represented by the generalization region. When the location generalization is not enough to satisfy the \(k_{mn}\)-anonymity, then we use time generalization. Finally, the performance of our algorithm is evaluated by the experiment and the validity of our algorithm is verified.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Xindi, M.A., Hui, L.I., Jianfeng, M.A., et al.: APPLET: a privacy-preserving framework for location-aware recommender system. Sci. China (Information Sciences) 60(9), 092101 (2017)
Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(5), 557–570 (2002)
Machanavajjhala, A., et al.: L-diversity:privacy beyond k-anonymity. In: Proceedings of 22nd International Conference on Data Engineering (ICDE’06), Atlanta, Georagia, USA, pp. 24–36 (2006)
Li, N., Li, T., Venkatasubraman, S.: t-Closeness: privacy beyond k-anonymity and l-diversity. In: IEEE, International Conference on Data Engineering, pp. 106–115. IEEE (2007)
Kellaris, G., Papadopoulos, S., Xiao, X., et al.: Differentially private event sequences over infinite streams. Proc. Vldb Endow. 7(12), 1155–1166 (2014)
Nergiz, E., Atzori, M., et al.: Towards trajectory anonymization: a generalization-based approch. Trans. Data Priv. 2(1), 47–75 (2009)
Rajaei, M., Haghjoo, M.S., Miyaneh, E.K.: Ambiguity in social network data for presence, sensitive-attribute, degree and relationship privacy protection. Plos One 10(6) (2015)
Feng, Z., Tan, H,, Shen, H.: Relationship privacy protection for mobile social network. In: International Conference on Advanced Cloud and Big Data, pp. 215–220. IEEE (2017)
Cicek, A.E., Nergiz, M.E., Saygin, Y.: Ensuring location diversity in privacy-preserving spatio-temporal data publishing. VLDB J. 23, 609–625 (2014)
Cranshaw, J., Toch, E., Hong, J., Kittur, A., Sadeh, N.: Bridging the gap between physical location and online social networks. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing, pp. 119–128. ACM (2010)
Acknowledgments
The research described in this paper was supported by the National Natural Science Foundation of China (U1401256), the National Natural Science Foundation of Liaoning province (201602094) and the Fundamental Research Funds for the Central Universities (3132018191).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yang, Z., Yang, M., Ning, B. (2020). User Relationship Privacy Protection on Trajectory Data. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 517. Springer, Singapore. https://doi.org/10.1007/978-981-13-6508-9_126
Download citation
DOI: https://doi.org/10.1007/978-981-13-6508-9_126
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6507-2
Online ISBN: 978-981-13-6508-9
eBook Packages: EngineeringEngineering (R0)