Abstract
Without considering the spatio-temporal features of check-in data, there exists privacy leakage risk for the sensitive relationships in social networks. In this paper, a Spatio-Temporal features based Graph Anonymization algorithm (denoted as STGA) is proposed, in order to protect sensitive relationships in social networks. We firstly devise a relationship inference algorithm based on users’ neighborhoods and spatio-temporal features of check-in data. Then, in STGA, we propose an anonymizing method through suppressing the edges or check-in data to prevent the inference of sensitive relationships. We adopt a heuristic to obtain the inference secure graph with high data utility. A series of optimizing strategies are designed for the process of edge addition and graph updating, which reduce the information loss meanwhile improving the efficiency of the algorithm. Extensive experiments on real datasets demonstrate the practicality, high data utility and efficiency of our methods.
The work is partially supported by the National Natural Science Foundation of China (Nos. 61502316, 61502317, 61702344), Key Projects of Natural Science Foundation of Liaoning Province (No. 20170520321).
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References
Cheng, J., Fu, W.C., Liu, J.: K-isomorphism: privacy preserving network publication against structural attacks. In: SIGMOD, pp. 459–470 (2010)
Jiang, H., Zhan, Q., Liu, W., Hai, Y.: Clustering-anonymity approach for privacy preservation of graph data-publishing. J. Softw. 2323–2333 (2017)
Liu, X., Yang, X.: Protecting sensitive relationships against inference attacks in social networks. In: Lee, S., Peng, Z., Zhou, X., Moon, Y.-S., Unland, R., Yoo, J. (eds.) DASFAA 2012. LNCS, vol. 7238, pp. 335–350. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29038-1_25
Huo, Z., Meng, X., Zhang, R.: Feel free to check-in: privacy alert against hidden location inference attacks in GeoSNs. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds.) DASFAA 2013. LNCS, vol. 7825, pp. 377–391. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37487-6_29
Heatherly, R., Kantarcioglu, M., Thuraisingham, B.: Preventing private information inference attacks on social networks. TKDE 25, 1849–1862 (2013)
Wang, Y., Zheng, B.: Preserving privacy in social networks against connection fingerprint attacks. In: ICDE, pp. 54–65 (2015)
Fu, Y., Zhang, M., Feng, D.: Attribute privacy preservation in social networks based on node anatomy. J. Softw. 25, 768–780 (2014)
Huo, Z., Meng, X., Hu, H., Huang, Y.: You can walk alone: trajectory privacy-preserving through significant stays protection. In: Lee, S., Peng, Z., Zhou, X., Moon, Y.-S., Unland, R., Yoo, J. (eds.) DASFAA 2012. LNCS, vol. 7238, pp. 351–366. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29038-1_26
Wang, W., Ying, L., Zhang, J.: The value of privacy: strategic data subjects, incentive mechanisms and fundamental limits. In: SIGMETRICS, pp. 249–260 (2016)
Abawajy, J., Ninggal, M., Herawan, T.: Privacy preserving social network data publication. Commun. Surv. Tutor. 18, 1974–1997 (2016)
Karwa, V., Raskhodnikova, S., Yaroslavtsev, G.: Private analysis of graph structure. Trans. Database Syst. 1146–1157 (2014)
Wang, W., Ying, L., Zhang, J.: On the relation between identifiability, differential privacy, and mutual-information privacy. IEEE Trans. Inf. Theory 62, 5018–5029 (2016)
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Liu, X., Li, M., Xia, X., Li, J., Zong, C., Zhu, R. (2018). Spatio-Temporal Features Based Sensitive Relationship Protection in Social Networks. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds) Web Information Systems and Applications. WISA 2018. Lecture Notes in Computer Science(), vol 11242. Springer, Cham. https://doi.org/10.1007/978-3-030-02934-0_31
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DOI: https://doi.org/10.1007/978-3-030-02934-0_31
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