Document Type
Article
Publication Date
2-6-2019
Abstract
Social network data is widely shared, forwarded and published to third parties, which led to the risks of privacy disclosure. Even thought the network provider always perturbs the data before publishing it, attackers can still recover anonymous data according to the collected auxiliary information. In this paper, we transform the problem of de-anonymization into node matching problem in graph, and the de-anonymization method can reduce the number of nodes to be matched at each time. In addition, we use spectrum partitioning method to divide the social graph into disjoint subgraphs, and it can effectively be applied to large-scale social networks and executed in parallel by using multiple processors. Through the analysis of the influence of power-law distribution on de-anonymization, we synthetically consider the structural and personal information of users which made the feature information of the user more practical.
Publication Title
Procedia Computer Science
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Sun, Q.,
Yu, J.,
Jiang, H.,
Chen, Y.,
&
Cheng, X.
(2019).
De-anonymyzing scale-free social networks by using spectrum partitioning method.
Procedia Computer Science,
147, 441-445.
http://doi.org/10.1016/j.procs.2019.01.262
Retrieved from: https://digitalcommons.mtu.edu/cs_fp/12
Version
Publisher's PDF
Publisher's Statement
© 2019 The Authors. Published by Elsevier B. V. Publisher's version of record: https://doi.org/10.1016/j.procs.2019.01.262