On prevention of attribute disclosure and identity disclosure against insider attack in collaborative social network data publishing Online publication date: Mon, 03-Jul-2023
by Bintu Kadhiwala; Sankita J. Patel
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 23, No. 1, 2023
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.
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