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Users can obtain intelligent services by sharing information in social networks. Big data technologies can discover underlying benefits from this information. However, stringent security concern is raised at the same time. The public data can be utilized by adversaries, which will bring dire consequences. In this paper, the influence maximization problem is investigated in a privacy protection environment, which aims to find a subset of secure users that can make the spread of influence maximization and privacy disclosure minimization. At first, in order to estimate the risk level for each user, a Bayesian-based individual privacy risk evaluation model is proposed to rank the individual risk levels. Secondly, as the aim is to measure the influence capability for each user, a cascade influence capability evaluation model is designed to rank the friend influence capability levels. Finally, based on these two factors, a privacy protection method is designed for solving the influence maximization with attack constraint problem. In addition, the comparison experiments show that our method can achieve the goal of influence maximization and privacy disclosure minimization efficiently.

Individual Attribute and Cascade Influence Capability-Based Privacy Protection Method in Social Networks