Title: On prevention of attribute disclosure and identity disclosure against insider attack in collaborative social network data publishing

Authors: Bintu Kadhiwala; Sankita J. Patel

Addresses: Department of Computer Science and Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India ' Department of Computer Science and Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India

Abstract: In a collaborative social network data publishing setup, privacy preservation of individuals is a vital issue. Existing privacy-preserving techniques assume the existence of attackers from external data recipients and hence, are vulnerable to insider attack performed by colluding data providers. Additionally, these techniques protect data against identity disclosure but not against attribute disclosure. To overcome these limitations, in this paper, we address the problem of privacy-preserving data publishing for collaborative social network. Our motive is to prevent both attribute and identity disclosure of collaborative social network data against insider attack. For the purpose, we propose an approach that utilises p-sensitive k-anonymity and m-privacy techniques. Experimental outcomes affirm that our approach preserves privacy with a reasonable increase in information loss and maintains an adequate utility of collaborative social network data.

Keywords: collaborative social network data publishing; attribute disclosure; identity disclosure; insider attack; k-anonymity; m-privacy.

DOI: 10.1504/IJBIDM.2023.131793

International Journal of Business Intelligence and Data Mining, 2023 Vol.23 No.1, pp.14 - 49

Received: 09 Nov 2021
Accepted: 20 Dec 2021
Published online: 03 Jul 2023 *

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