Privacy Preserving Approaches for Online Social Network Data Publishing

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Author(s): Kamalkumar Macwan (Sardar Vallabhbhai National Institute of Technology, India)and Sankita Patel (Sardar Vallabhbhai National Institute of Technology, India)
Copyright: 2021
Pages: 14
Source title: Handbook of Research on Digital Transformation and Challenges to Data Security and Privacy
Source Author(s)/Editor(s): Pedro Fernandes Anunciação (Polytechnic Institute of Setúbal, Portugal), Cláudio Roberto Magalhães Pessoa (Escola de Engenharia de Minas Gerais, Brazil)and George Leal Jamil (Informações em Rede Consultoria e Treinamento, Brazil)
DOI: 10.4018/978-1-7998-4201-9.ch007

Keywords: Cyber and Network Security / Information Science Reference / Information Security & Privacy / Security & Forensics

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Abstract

Recently, the social network platforms have gained the attention of people worldwide. People post, share, and update their views freely on such platforms. The huge data contained on social networks are utilized for various purposes like research, market analysis, product popularity, prediction, etc. Although it provides so much useful information, it raises the issue regarding user privacy. This chapter discusses the various privacy preservation methods applied to the original social network dataset to preserve privacy against attacks. The two areas for privacy preservation approaches addressed in this chapter are anonymization in social network data publication and differential privacy in node degree publishing.

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