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A machine learning based approach for user privacy preservation in social networks

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Abstract

With the development of Internet technology, service providers can provide users with personalized services to enrich user experience, however, this often requires a large number of users’ private data. Meanwhile, the protection of their private data and the evaluation of the risk of leaked datasets become a matter of great concern to many people. To resolve these issues, in this paper, we develop a machine learning-based approach in online social networks (OSNs) to efficiently correlate the leaked datasets and accurately learn millions of users’ confidential information. Moreover, a trust evaluation model is developed in OSNs to identify malicious service providers and secure users’ social activities via direct trust computing and indirect trust computing. Extensive experiments are conducted by using real-world leaked datasets, and the results show that the efficiency and effectiveness of the proposed approach in terms of user privacy protection and accuracy of privacy leakage evaluation.

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Acknowledgements

The preliminary version of this article has been published in CyberSciTech 2019. This research is supported in part by National Key R&D Program of China (2018YFC0830500), National Natural Science Foundation of China (U1736205, 61603290), Shenzhen Basic Research Grant (JCYJ20170816100819428), Natural Science Basic Research Plan in Shaanxi Province of China (2019JM-159), and Natural Science Basic Research in Zhejiang Province of China (LGG18F020016).

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    Authors and Affiliations

    1. MOE Key Laboratory for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, China

      Yuanming Zhang, Jing Tao, Shuo Zhang, Yuchao Zhang & Pinghui Wang

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    1. Yuanming Zhang
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    2. Jing Tao
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    3. Shuo Zhang
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    Correspondence to Yuanming Zhang.

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    Yuanming Zhang and Jing Tao contributed equally to this work.

    This article belongs to the Topical Collection: Special Issue on Privacy-Preserving Computing

    Guest Editors: Kaiping Xue, Zhe Liu, Haojin Zhu, Miao Pan and David S.L. Wei

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    Zhang, Y., Tao, J., Zhang, S. et al. A machine learning based approach for user privacy preservation in social networks. Peer-to-Peer Netw. Appl. 14, 1596–1607 (2021). https://doi.org/10.1007/s12083-020-01068-0

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