| Summary: | Current relevant researches on influence propagation of social networks focus on how to use a small size seed set to produce the highest impact in social networks, and they often regard forwarding as the only way of information diffusion, ignoring other ways of information diffusion. For example, users can disseminate information by publishing a message with similar content to the message they see. This way of diffusion (referred to as mentioning) is difficult to track, and it is easy to cause the risk of privacy disclosure. Aiming at the causes of privacy leakage in social networks, this paper defines a social network information diffusion model supporting mentioning relationship, and presents a social network information diffusion algorithm LocalGreedy, which can ensure messages sent by users are not leaked to the specified, maximize the influence of the propagation and balance the contradiction between privacy protection and message propagation. This paper proposes an incremental strategy to construct a seed set while reducing time complexity caused by enumeration. After that, giving the calculating method on local influence subgraph, the influence generated by seed set propagation can be quickly estimated. When estimating the influence, a calculation method for deriving the upper limit of privacy leakage probability is proposed to ensure the privacy protection constraint limit and avoid time complexity caused by the Monte Carlo simulation. The crawled Sina Weibo dataset is used to carry out experimental verification and example analysis. The experimental results show that the proposed method is effective.
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