Title: A novel privacy protection method based on node segmentation for social networks

Authors: Zhongli Wang; Aiyun Ju

Addresses: School of Electrical Engineering, Zhengzhou University of Science and Technology, Zhengzhou, 450064, China ' Department of Mechanical and Electrical Engineering, Zhengzhou Institute of Technology, Zhengzhou, 451150, China

Abstract: In order to solve the problem of privacy disclosure of weight sequence in weighted social networks privacy protection, this paper proposes an anonymous weighted sequence method based on node segmentation to realise privacy protection of network structure, edge weight and weight sequence. In this method, the anonymity of edge weight sequence is realised mainly through the diameter distance within the group and the relative distance between nodes, which makes up for the privacy disclosure of weight sequence and improves the privacy protection mechanism of weighted social network. Under the premise of privacy security, this method can guarantee the structural features needed for social network analysis, the validity of the published data and the effective resistance to the attack of weight sequence. We also make comparison with other methods in terms of execution time and recognition rate, the results show that the proposed method can obtain shorter time and high node recognition.

Keywords: social networks; privacy protection; node segmentation; diameter distance.

DOI: 10.1504/IJCNDS.2022.123857

International Journal of Communication Networks and Distributed Systems, 2022 Vol.28 No.4, pp.459 - 475

Received: 26 Jul 2021
Accepted: 20 Aug 2021
Published online: 04 Jul 2022 *

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