# Graph Convolutional Networks for Privacy Metrics in Online Social Networks
## 2 Related Work
analysed possible aspects of privacy leakage in social networking sites and constructed a framework based on user groups, information control and posts, information deletion and reply, and other aspects to measure the privacy scores of multiple social networking sites [20]. [PERSON] et al. propose a Privacy Policies Recommended System (PPRS) that assists the users in defining their own privacy policies. The proposed system is able to exploits a certain set of properties (or attributes) of the users to define permissions on the shared contents [21]. However, in recent years, the application of graph neural networks in other fields has been far ahead of the direction of privacy protection, which often causes privacy threats. In [22], the author applies GNNs to social recommendation, which can perform social recommendation in heterogeneous networks, but it can also infer some characteristics of the target users from the recommended users. [23] used GNNs to link identities of users in multiple social networks. Although GNNs can provide more services for users, it also increases the malicious behaviour of identity inference. [PERSON] et al. [24] proposed a framework for using a convolutional neural network (CNN) to carry out inference attacks on image and attribute information. Experiments showed that it can effectively and accurately infer attribute information that users do not disclose. At the same time, [PERSON] et al. [25] proposed a method of using a CNN to infer missing information in social network personal data; the effect is very excellent, but the missing information may be deliberately hidden by users. We hope that we can also use neural networks to help users improve their privacy awareness, cultivate their privacy behaviour, and have the ability to fight against these methods that may cause privacy disclosure. To date, the privacy metrics of social networks have remained at the stage of using basic mathematical calculations to quantify multiple aspects of privacy, but the hidden relationship between these aspects is ignored. Meanwhile, these methods are inefficient, which means that in most studies, the whole network where users are located is analysed and quantified, but only the privacy score of the target user is obtained, which is undoubtedly a waste of resources. Related fields have entered the area of artificial intelligence, but the development of artificial intelligence in privacy measurement is slow. To address the above problems, we innovatively introduce deep learning model GCNs to obtain users' privacy metrics on social networks. Graph convolution networks (GCNs) [8] are an efficient variant of convolutional neural networks (CNNs) on graphs. A GCN stacks layers of learned first-order spectral filters followed by a nonlinear activation function to learn graph representations. Recently, GCNs and their subsequent variants have made state-of-the-art achievements in various application fields, including but not limited to citation networks [8], social networks [26], applied chemistry [27], natural language processing [28; 29; 30], and computer vision [31; 32]. Few-shot learning is a method to learn a unique set of new classes by using a few labelled data in a set of base classes without overfitting. A group of mature applications have emerged: [PERSON] et al. [33] follow a semi-supervised learning setup but use graph-based label propagation (LP) [34] for classification and jointly use all test images. [PERSON] et al. [35] extended this method to a wider range by using a large number of unlabelled images in the set of images for learning without using additional text information. In the research of [PERSON] et al. [36], a method of using GCNs and few-shot learning to identify a new class of image is proposed, which inspires us. In the field of privacy security, labelled data are scarce, and experts with relevant specialties are required to finish this work, which greatly limits research in deep learning. Therefore, we introduce few-shot learning to solve this problem.