Toward pragmatic modeling of privacy information propagation in online social networks

The growing popularity of online social networks (OSNs) in recent years has generated a lot of concern on personal privacy. One approach of protecting privacy in OSNs is to intervene in the flow of privacy information, making the study of the dynamics of privacy information propagation necessary for the design of effective privacy protection mechanisms. Although previous work on information propagation has produced some models, these models are not adequate for privacy information since they do not reflect the main characteristics of privacy information. In this paper, we propose a model for privacy information propagation. We first analyze the structural characteristics of privacy information and then design the model by incorporating these characteris-tics. A unique feature of the model is that it infers the privacy attitudes of the information recipients to the privacy concerning subject implicated in the privacy information to determine the forwarding decisions of the recipients. Thus, by mapping the heterogeneous tendency of information forwarding by the recipients to a limited number of privacy attitudes, the model can predict the decisions on forwarding privacy information and thus describe the macroscopic process of privacy information propagation. Results of the experiment based on real OSN datasets show that the proposed model can be used to learn both the scope and the trend of privacy information propagation in OSNs, demonstrating the importance of the privacy attitudes of recipients on privacy information propagation. The properties of the model are also studied through experiment to examine the impact of various factors on privacy information propagation in OSNs.