Modeling Privacy Leakage Risks in Large-Scale Social Networks

• Main Authors: Suguo Du, Xiaolong Li, Jinli Zhong, Lu Zhou, Minhui Xue, Haojin Zhu, Limin Sun
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Social network services; Data privacy; Information security
• Online Access: https://ieeexplore.ieee.org/document/8322239/

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 realworld data sets, triggered by the survey of over 300 volunteers involved, which illuminates the privacy risk management of contemporary social network service providers.