Abstract
Privacy inference imposes a serious threat to user privacy in Online Social Networks (OSNs) as the vast amount of personal data and relationships in OSNs can be used not only to infer user privacy but also to enrich the training set of inference methods. Previous studies have mostly focused on privacy inference from the perspective of the adversary with the objective of tracking the accuracy of the inference results. However, countering privacy inference requires not only the analysis of the inference method but also the roles that data would play in the inference process, i.e., to determine the sensitivity of the data. To address this issue, in this paper, we propose a model for the identification of privacy-sensitive data in which we formulate the identification as an influence maximization problem to identify both the privacy-sensitive users and the privacy-sensitive attributes for privacy inference. In our model, a privacy-affected tree is first constructed based on the influence between users with respect to the concerned privacy. Then, privacy-sensitive users in the privacy-affected tree are identified along with their privacy-sensitive attributes based on their contribution to the inference result of the concerned privacy. Experiments show that the results of our proposed model can significantly affect the accuracy of privacy inference, which demonstrates that our model can identify the privacy-sensitive data. Meanwhile, the impact of privacy-sensitive users and privacy-sensitive attributes is analyzed to guide the design of effective privacy-enhancing technologies.
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References
Sun, Q., Xu, Y.: Research on privacy concerns of social network users. In: 2019 IEEE 5th International Conference on Computer and Communications (ICCC), pp. 1453–1460 (2019)
Wang, Z., Zhu, H., Sun, L.: Social engineering in cybersecurity: effect mechanisms, human vulnerabilities and attack methods. IEEE Access 9, 11895–11910 (2021)
Al-Dablan, D., Al-Hamad, A., Al-Bahlal, R., Badawi, M.A.: An analysis of various social engineering attack in social network using machine learning algorithm. Int. J. Distrib. Sens. Networks. 46 (2020)
Piao, Y., Ye, K., Cui, X.: Privacy inference attack against users in online social networks: a literature review. IEEE Access 9, 40417–40431 (2021)
Jia, J., Wang, B., Zhang, L., Gong, N.Z.: AttriInfer: inferring user attributes in online social networks using Markov random fields (2017)
Gong, N.Z., Liu, B.: Attribute inference attacks in online social networks. ACM Trans. Priv. Secur. 21, 3 (2018)
Mao, J., Tian, W., Yang, Y., Liu, J.: An efficient social attribute inference scheme based on social links and attribute relevance. IEEE Access 7, 153074–153085 (2019)
Tian, Y., Niu, Y., Yan, J., Tian, F.: Inferring private attributes based on graph convolutional neural network in social networks. In: Proceedings of the 2019 International Conference on Networking and Network Applications. NaNA 2019, pp. 186–190 (2019)
Wu, Y., Lian, D., Jin, S., Chen, E.: Graph convolutional networks on user mobility heterogeneous graphs for social relationship inference. In: IJCAI International Joint Conference on Artificial Intelligence, 2019-August, pp. 3898–3904 (2019)
Luo, X., Xie, M., Zhang, K., Zhou, F., Zhong, T.: DeepAttr: inferring demographic attributes via social network embedding. IEEE Access 7, 130270–130282 (2019)
Sutton, C., McCallum, A.: An introduction to conditional random fields. Found. Trends Mach. Learn. 4, 267–373 (2011)
Heatherly, R., Kantarcioglu, M., Thuraisingham, B.: Preventing private information inference attacks on social networks. IEEE Trans. Knowl. Data Eng. 25, 1849–1862 (2013)
Qian, J., Li, X.Y., Zhang, C., Chen, L., Jung, T., Han, J.: Social network de-anonymization and privacy inference with knowledge graph model. IEEE Trans. Dependable Secur. Comput. 16, 679–692 (2019)
Liu, Y., Li, N.: Retrieving hidden friends: a collusion privacy attack against online friend search engine. IEEE Trans. Inf. Forensics Secur. 14, 833–847 (2019)
Borgs, C., Brautbar, M., Chayes, J., Lucier, B.: Maximizing social influence in nearly optimal time. Soc. Ind. Appl. Math. (2014)
Wang, X., Zhang, Y., Zhang, W., Lin, X., Chen, C.: Bring order into the samples: a novel scalable method for influence maximization. IEEE Trans. Knowl. Data Eng. 29, 243–256 (2017)
Song, C., Hsu, W., Lee, M.L.: Targeted influence maximization in social networks. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 1683–1692 (2016)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Annu. Rev. Sociol. 27, 415–444 (2001)
Mcauley, J.: Learning to discover social circles in ego networks. In: NIPS2012, pp. 1–9 (2012)
Tang, J., Tang, X., Xiao, X., Yuan, J.: Online processing algorithms for influence maximization. In: Proceedings of the ACM SIGMOD International Conference on Management Data, pp. 991–1005 (2018)
Tang, J., Tang, X., Yuan, J.: Profit maximization for viral marketing in online social networks: algorithms and analysis. IEEE Trans. Knowl. Data Eng. 30(6), 1095–1108 (2017)
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Yi, Y., Zhu, N., He, J., Ma, X., Luo, Y. (2023). Priv-S: Privacy-Sensitive Data Identification in Online Social Networks. In: Zhang, F., Wang, H., Barhamgi, M., Chen, L., Zhou, R. (eds) Web Information Systems Engineering – WISE 2023. WISE 2023. Lecture Notes in Computer Science, vol 14306. Springer, Singapore. https://doi.org/10.1007/978-981-99-7254-8_17
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DOI: https://doi.org/10.1007/978-981-99-7254-8_17
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