Abstract
The increasing popularity of social networks has inspired recent research to explore social graphs for data mining. Because social graph data contains sensitive information about users, publishing the graph data directly will cause privacy leakage of users. In this paper, we assume that attackers might re-identify targets with 1-neighborhood graph attacks. To prevent such attacks, we propose a Graph Matching based Privacy-preserving Scheme, named GMPS, to anonymize the social graphs. We utilize Jensen-Shannon Divergence to compute node structure similarity to improve the accuracy of node clustering. And then, utilize the graph modification to achieve k-anonymity and use graph matching to measure the similarity of graphs. The experiment results on HepTh and Facebook show that the proposed approach achieves k-anonymity with low information loss and high data utility.
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Acknowledgments
The authors would like to thank the National Science Foundation of China (Nos. U1905211, 61771140, 61702100, 61702103), Fok Ying Tung Education Foundation (No. 171061), Natural Science Foundation of Fujian Province (No. 2020J01167), Educational Research Project for Young and Middle-aged Teachers in Fujian Province (Science and Technology) (No. JAT200968).
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Zhang, H., Li, X., Xu, J., Xu, L. (2021). Graph Matching Based Privacy-Preserving Scheme in Social Networks. In: Lin, L., Liu, Y., Lee, CW. (eds) Security and Privacy in Social Networks and Big Data. SocialSec 2021. Communications in Computer and Information Science, vol 1495. Springer, Singapore. https://doi.org/10.1007/978-981-16-7913-1_8
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DOI: https://doi.org/10.1007/978-981-16-7913-1_8
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