Title: Study and analysis of data anonymisation techniques for social networks

Authors: K. Sivasankari; K.M. Uma Maheswari

Addresses: Faculty of Engineering and Technology, Department of CSE, SRM Institute of Science and Technology, Ramapuram, 600079, Tamilnadu, India ' Faculty of Engineering and Technology, Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India

Abstract: Many people all across the globe have been utilising social media to exchange information. Numerous firms apply social data mining to extract numerous exciting insights from social data which is represented as a sophisticated network structure. However, publishing social data has a direct and indirect influence on the privacy of many of its users. Recently, numerous anonymisation methods have been invented and deployed to preserve sensitive information about users and their interactions on social media. This paper presents a complete assessment of several data anonymisation algorithms for social network data and evaluates their pros and downsides. It also tackles the primary research problems surrounding the effectiveness of anonymisation technologies.

Keywords: data anonymisation; social networks; graph modification; machine learning; privacy; social data; data mining.

DOI: 10.1504/IJSSE.2024.143703

International Journal of System of Systems Engineering, 2024 Vol.14 No.6, pp.605 - 632

Received: 07 Feb 2023
Accepted: 09 May 2023
Published online: 06 Jan 2025 *

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