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Type: Journal article
Title: Modeling privacy leakage risks in large-scale social networks
Author: Du, S.
Li, X.
Zhong, J.
Zhou, L.
Xue, M.
Zhu, H.
Sun, L.
Citation: IEEE Access, 2018; 6:17653-17665
Publisher: IEEE
Issue Date: 2018
ISSN: 2169-3536
Statement of
Suguo Du, Xiaolong Li, Jinli Zhong, Lu Zhou, Minhui Xue, Haojin Zhu, and Limin Sun
Abstract: The current culture that encourages online dating, and interaction makes large-scale social network users vulnerable to miscellaneous personal identifiable information leakage. To this end, we take a first step toward modeling privacy leakages in large-scale social networks from both technical and economic perspectives. From a technical perspective, we use Markov chain to propose a dynamic attack-defense tree-based model, which is temporal-aware, to characterize an attack effort made by an attacker and a corresponding countermeasure responded by a social network security defender. From an economic perspective, we use static game theory to analyze the ultimate strategies taken by the attacker and the defender, where both rational participants tend to maximize their utilities, with respect to their attack/defense costs. To validate the proposed approach, we perform extensive experimental evaluations on three real-world data sets, triggered by the survey of over 300 volunteers involved, which illuminates the privacy risk management of contemporary social network service providers.
Keywords: Social network services; data privacy; information security
Rights: © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See for more information.
DOI: 10.1109/ACCESS.2018.2818116
Appears in Collections:Aurora harvest 4
Computer Science publications

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