# Graph Convolutional Networks for Privacy Metrics in Online Social Networks
## 1 Introduction  
With the rapid development of the Internet, the cost for ordinary users to use the network is decreasing. The Internet has entered an era in which the whole populace are netizens. The Internet has become the most important way for people to obtain and release information, for which purpose, social networks are the most popular websites. However, in recent years, the privacy literacy of users has been unable to support their privacy protection behaviour [1], and an increasing number of privacy disclosure events have occurred; Facebook provided 50 million pieces of user data to the Cambridge company, which affected the American election in March 2018, and in November, due to the opening of the API (application programming interface) to third-party applications, 68 million users' mobile phone photo information was leaked. In 2017, Twitter announced that it was giving up its DNT (do not track) privacy protection policy. Tumblr disclosed 65 million users' email accounts andpasswords in May 2016. In addition, a hacker organisation called \"peace\" sold 167 million LinkedIn users' login information on the black market at the price of five bitcoins (approximately $2200). On 19 October 2016, the NetEase user database was leaked, and more than 100 million users' 163 and 126 mailbox information was leaked, including usernames, passwords, password security information, login IPs, and user birthdays. These privacy leakages may lead to a series of malicious acts [2; 3; 4; 5], including but not limited to tracking, defamation, spam, phishing, identity theft, personal data cloning, Sybil attack, etc. However, academia has always focused on large-scale industrial networks, such as the Internet of Things, smart grids, and cloud storage networks, ignoring the privacy protection of individual users because the privacy leakage of large-scale networks causes substantial visible economic losses, but the impact of personal information leakage is scattered and small. Moreover, users' use of social networks is a process of sharing public information. Applying privacy protection methods to traditional industrial networks will damage user experience. The existing privacy protection methods in social networks include anonymity, decentralisation, encryption, information security regulations, fine-grained privacy settings and access control, and improving user privacy awareness and privacy behaviour [6]. The first four methods need to rely on operators, which has proven to be unreliable. With the rapid development of attribute inference, link inference, personal identification, and community discovery, the role of anonymisation is becoming weaker and weaker. As long as users use social networks, they are disclosing information, so the adaptability of encryption is poor. Fine-grained privacy settings and access control rely on the user's own privacy literacy, but according to the research, less than one-third of users have changed these settings, and most of the settings are not reasonable. Therefore, the most fundamental solution is to help users cultivate their privacy awareness and enhance their privacy protection behaviour. Privacy metrics are the best way to achieve this goal. These metrics quantify all aspects of the ways that users may disclose private information on social networks and transform the virtual concept of privacy into specific values in the physical space so that users can intuitively understand their privacy status. If they are not satisfied with their current privacy status, they can continuously adjust it according to the privacy score, enhance their privacy awareness in the process of adjustment, and cultivate behavioural habits of privacy protection. The method of traditional privacy metrics basically uses mathematical calculations to obtain quantitative statistics on all the aspects that affect users' privacy disclosure, including but not limited to attribute information, network environment information, trust between users, and publishing information content. However, these approaches face two problems.