Open Access

ARTICLE

A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

Hao Jiang¹, Yuerong Liao¹, Dongdong Zhao², Wenjian Luo³, Xingyi Zhang^1,*

1 Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, 230601, China
2 Chongqing Research Institute, School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, China
3 Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518055, China

* Corresponding Author: Xingyi Zhang. Email:

(This article belongs to the Special Issue: Privacy-Preserving Technologies for Large-scale Artificial Intelligence)

Computer Modeling in Engineering & Sciences 2024, 140(1), 1045-1075. https://doi.org/10.32604/cmes.2024.048653

Received 14 December 2023; Accepted 21 February 2024; Issue published 16 April 2024

Abstract

Due to the presence of a large amount of personal sensitive information in social networks, privacy preservation issues in social networks have attracted the attention of many scholars. Inspired by the self-nonself discrimination paradigm in the biological immune system, the negative representation of information indicates features such as simplicity and efficiency, which is very suitable for preserving social network privacy. Therefore, we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks, called AttNetNRI. Specifically, a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept private. Moreover, a negative database-based method is proposed to hide node attributes, so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes, which is crucial to the analysis of social networks. To evaluate the performance of the AttNetNRI, empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social networks. The experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding parts. The experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

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Keywords

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