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.
Location-based services have been widely used in daily life, providing diversified services for users. However, users may face the risk of trajectory privacy disclosure while enjoying the convenience of location-based services. Most of the existing trajectory protection schemes cannot match the road network and are vulnerable to attacks based on background information. In this paper, the concept of salp swarm algorithm is introduced to construct salp-like swarm algorithm, which can generate K−1 false trajectories that are highly similar to real trajectories. It is difficult for attackers to distinguish them. Besides, a road network matching model is designed in order to match the proposed trajectory privacy protection algorithm with the real road network environment, so that the effect of trajectory privacy protection is improved. Morever, a false location selection mechanism is proposed to find false location points, which not only considers the location and speed of users, but also ensures that the selection of false location points is more in line with the road network environment. The experimental results show that, under the condition of satisfying the same service quality, the trajectory privacy leakage probability of this scheme is reduced by 33% compared with the existing schemes, and it has better privacy protection effect.
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