Social influence-based privacy inference attacks in online social networks

Privacy inference attacks in online social networks aim at inferring hidden attributes of target users based on released attributes of the users as well as information about social relationships in the networks. Although many privacy inference methods have been proposed in recent years, most of the methods have mostly focused on pursuing high accuracy of the inference results without paying too much attention on identifying users who make the most contributions to the inference, making it less useful in the development of effective countermeasures. In this paper, by taking full consideration of the roles of users in privacy inference, we propose a privacy inference method based on graph attention networks that can provide information about not only the private attributes of the target users, but also the importance of the users in the process of inferring the results. We also design a novel mechanism for the selection of influential users to make our proposed method adaptive to the characteristics of user influence diffusion, resulting in involving fewer number of users in privacy inference and hence improving the efficiency. We also performed some experiment to evaluate the proposed method using a real online social network dataset to show the effectiveness of the proposed method. Comparison with other comparable methods also shows that the results of our proposed method are more accurate.