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Entropy-driven differential privacy protection scheme based on social graphlet attributes

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Abstract

The exponential growth of social networks has resulted in the generation of vast amounts of graph data containing sensitive information. However, the exposure of such data could lead to disastrous consequences. Current graph data protection algorithms lack sufficient research on the attribute characteristics of social users, which results in a failure to safeguard potentially vulnerable private data effectively. To address these issues, an entropy-driven differential privacy protection scheme based on social graphlet attributes (EDP-SGA) is proposed. Firstly, a matrix-based algorithm is proposed for constructing an attribute intimacy matrix, which can quantify the strength of links among social users’ attributes. Secondly, an influence algorithm based on user node attributes and information entropy is proposed, which can divide social networks into communities and select seed nodes. Thirdly, a privacy-preserving social network data publishing algorithm is proposed, which can combine graph modification techniques and differential privacy to convert sensitive graph data into an uncertain graph. Finally, experimental results demonstrate that the EDP-SGA can keep the balance between the privacy and the utility of social graph data.

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Acknowledgements

The authors would like to thank the National Natural Science Foundation of China (No. 61902069) and the Natural Science Foundation of Fujian Province of China (2021J011068).

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Authors and Affiliations

  1. School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, 350118, Fujian, China

    Jing Zhang, Zuan-yang Zeng & Kun-liang Si

  2. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fuzhou, 350118, China

    Jing Zhang, Zuan-yang Zeng & Kun-liang Si

  3. Department of Computer Science, University of Tsukuba, Tsukuba, 3058573, Japan

    Xiu-cai Ye

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  1. Jing Zhang
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Zhang, J., Zeng, Zy., Si, Kl. et al. Entropy-driven differential privacy protection scheme based on social graphlet attributes. J Supercomput 80, 7399–7432 (2024). https://doi.org/10.1007/s11227-023-05751-w

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