Title: Privacy-preserving collaborative social network data publishing against colluding data providers

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: Preserving individuals' privacy is a vital issue in a collaborative social network data publishing setup. Existing data publishing approaches for collaborative social network data prevent identity disclosure and attribute disclosure performed by external data recipients only. These approaches are vulnerable to insider attack - an attack performed by colluding data providers. In an insider attack, attackers' malicious intent is to compromise the privacy of those individuals whose data records are contributed by other data providers. Additionally, existing approaches utilise generalisation and/or suppression operations to preserve privacy and hence, do not retain the data originality that eventually results in compromise of the data utility. In this paper, we present an approach that overcomes these shortcomings aiming to prevent identity disclosure and attribute disclosure of collaborative social network data against insider attack and to provide higher utility. Experimental outcomes affirm the security of the approach with adequate data utility.

Keywords: collaborative social network data publishing; insider attack; m-privacy; k-anonymity; l-diversity.

DOI: 10.1504/IJICS.2022.127174

International Journal of Information and Computer Security, 2022 Vol.19 No.3/4, pp.346 - 378

Received: 03 Oct 2021
Accepted: 12 Jan 2022
Published online: 23 Nov 2022 *

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